Techniques August 2017

contents

Volume 11 Number 1 / August 2017

in every issue

4 A Letter from the President 5 USTFCCCA Presidents

8 FEATURES

8 Pacing Strategy

Can analytics help us run faster in cross-country?

By Stephen Lane

16 The Long Jump

Physical backgrounds of the long jump for coaches and athletes

By Vladimir Strelnitski, Max Coleman and Bill Ferguson

26 How Teams Fail

Is your program headed towards destruction?

Marques R. Dexter, Ashley Fallaize, Nicole McCluney and Dr. Paul Schempp

34 Heavy Hammer

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A study in the correlation between hammer throw and weight throw performance and development for U.S. collegiate throwers

Donald G. Babbitt and Luke E. Johnson

48 Skill Acquisition

Evidence-based Practices for Detecting and Correcting Errors

Matthew T. Buns, Ph.D. and Tyler Naumowicz

AWARDS

62 2017 Outdoor Track & Field National

Coaches and Athletes of the Year

COVER

Photograph courtesy of Kirby Lee

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A LETTER FROM THE PRESIDENT Publisher Sam Seemes Executive Editor Mike Corn Contributing Editor Kristina Taylor

A

s the summer is drawing to a close, I want to applaud all of you who spent time this summer working on your professional development as a coach in our sports. Many of you took courses in our USTFCCCA Track & Field Academy to further develop your knowledge and skills as a coach, and this year, we were pleased to award our first-ever Track & Field Academy Master’s Endorsement to two coaches. Nate Hoey of Williams College earned a Master’s Endorsement in the Long Sprints, and Dennis Newell of the University of Mary earned a Master’s Endorsement in Cross Country. A fine job by both of these coaches!

This summer also saw the announcement of the 2017 Men’s and Women’s Finalists for The Bowerman, and what an outstanding group of studentathletes we have to celebrate this year! Maggie Ewen (Arizona State), Keturah Orji (Georgia), and Raevyn Rogers (Oregon) are our Women’s Finalists, and Christian Coleman (Tennessee), Fred Kerley (Texas A&M), and Lindon Victor (Texas A&M) are our Men’s Finalists. Thank you to all of you who participated in our fan voting this summer; we had nearly 75,000 votes cast between our Men’s and Women’s Finalists. I’m sure you are as excited as I am to see the Men’s and Women’s winners of The Bowerman announced at the 2017 USTFCCCA Convention!

On another awards-related note, as this issue of techniques goes to press, the announcement of our 2017 class of the USTFCCCA Coaches Hall of Fame is being prepared for release. I know that all of us look forward to honoring another outstanding group of coaches with induction into our Hall of Fame at the 2017 USTFCCCA Convention in December Speaking of the USTFCCCA Convention, it’s not too early to begin making plans to join us this year. The 2017 USTFCCCA Convention will be held December 12-15 at the JW Marriott Desert Ridge Resort and Spa in Phoenix, AZ. Visit our website at www.ustfccca.org for convention details, registration, hotel information and more. I am confident that our work together at the USTFCCCA Convention can make our sports better for student-athletes, coaches and fans.

As the 2017 Cross Country season gets underway, I want to wish everyone a successful fall season, whether you are in-season for Cross Country or preparing off-season for Indoor Track and Field. Stay tuned to the USTFCCCA website throughout the fall for coverage of the Cross Country season, USTFCCCA regional and national rankings, and USTFCCCA postseason awards. I hope you have a healthy and rewarding start to this new school year!

DENNIS SHAVER President, USTFCCCA Dennis Shaver is the head men’s and women’s track and field coach at Louisiana State University. Dennis can be reached at shaver@lsu.edu

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DIRECTOR OF MEDIA, BROADCASTING AND ANALYTICS Tom Lewis Membership Services Kristina Taylor communications

Tyler Mayforth, Curtis Akey Photographer Kirby Lee Editorial Board Tommy Badon, Todd

Lane, Boo Schexnayder, Derek Yush

Published by Renaissance Publishing LLC 110 Veterans Memorial Blvd., Suite 123, Metairie, LA 70005 (504) 828-1380 myneworleans.com

USTFCCCA

National Office 1100 Poydras Street, Suite 1750 New Orleans, LA 70163 Phone: 504-599-8900 Fax: 504-599-8909 Techniques (ISSN 1939-3849) is published quarterly in February, May, August and November by the U.S. Track & Field and Cross Country Coaches Association.

Copyright 2016. All rights reserved. No part of this publication may be reproduced in any manner, in whole or in part, without the permission of the publisher. techniques is not responsible for unsolicited manuscripts, photos and artwork even if accompanied by a self-addressed stamped envelope. The opinions expressed in techniques are those of the authors and do not necessarily reflect the view of the magazines’ managers or owners.

Periodical Postage Paid at New Orleans La and Additional Entry Offices. POSTMASTER: Send address changes to: USTFCCCA, PO Box 55969, Metairie, LA 70055-5969. If you would like to advertise your business in techniques, please contact Mike Corn at (504) 599-8900 or mike@ustfccca.org.

DIVISION PRESIDENTs DIVISION I Connie Price-Smith

Dave Smith

Connie Price-Smith is the head men’s and women’s track and field coach at the University of Mississippi. Connie can be reached at cmprices@olemiss.eduu

Dave Smith is the director of track and field and cross country at Oklahoma State University. Dave can be reached at dave.smith@okstate.edu

Ryan Dall

Jim Vahrenkamp

Ryan Dall is the head track and field and cross country coach at Texas A&M Kingsville. Ryan can be reached at ryan.dall@tamuk.edu

Jim Vahrenkamp is the Director of cross country and track & field at Queens University. Jim can be reached at vahrenkampj@queens.edu

Jason Maus

Dara Ford

NCAA Division 1 Track & Field

NCAA Division I Cross Country

DIVISION II NCAA Division II Track and Field

NCAA Division II Cross Country

DIVISION III Jason is the head cross country and track and field coach at Ohio Northern University and can be reached at j-maus@onu.edu

Dara is the head cross country and track and field coach at Otterbein University and can be reached at DFord@Otterbein.edu

Mike McDowell

Heike McNeil

Mike McDowell is the head men’s and women’s track and field coach at Olivet Nazarene University. Mike can be reached at mmcdowel@olivet.edu

Heike McNiel is the head track and field and cross county coach at Northwest Christian University. Heike Can be reached at hmcneil@nwcu.edu

Ted Schmitz

Don Cox

Ted Schmitz is the head track and field coach at Cloud County Community College.

Ted can be reached at tschmitz@cloud.edu

Don Cox is the head track and field and cross country coach at Cuyahoga Community College.

Don can be reached at donald.cox@tri-c.edu

NAIA NAIA Track & Field

NAIA Cross Country

njcaa NJCAA Track and Field

NJCAA Cross Country

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Pacing Strategy can analytics help us run faster in cross-country?

By Stephen Lane

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pacing strategy

O

ver the past decade, data analysis has sparked a revolution across all sports. Well-known “truths” have been gradually – often begrudgingly – overturned by unorthodox heresies that not so long ago were casually dismissed.

This dynamic burst into the popular consciousness with the book (and subsequent movie) Moneyball, about the low-budget Oakland A’s innovative approaches to competing with behemoths like the Yankees. In the NBA, analytics transformed offensive strategy: 3-point attempts – previously undervalued and under-utilized, now a primary weapon – have increased from 13.7 per game in 2000-01 to 24 in 2015-16 (basketball-reference.com). And in the NFL, recent analyses argue that teams should go for it on 4th down more often than they do (Romer, 2006). The efficacy of data-driven analysis has convinced most of the early doubters. Nearly all major-sport franchises now spend lavishly on analytics; those that don’t are derided as cave-dwelling adherents to stone age methods of scouting, analysis, and coaching. The question is no longer whether to invest in data analysis, but which approaches are most useful.

The purpose of this paper is to analyze the pacing strategies of athletes in championship collegiate cross-country races, for which there now exists a great deal of information. Basic statistical analysis suggests a very strong relationship between pacing and finishing time: relatively even pacing predicts faster times.

Or, more accurately, athletes with less positive splits run faster and place higher. While at first glance this seems an obvious and not particularly useful statement, it suggests that the vast majority of championship-level collegiate runners employ sub-optimal pacing. This in turn leads to an important question without an obvious answer: Why do highly-trained athletes, guided by experienced and highly-educated coaches – with every incentive to optimize performance – stray so far from good pacing?

High-level programs devote significant resources to nutritionists, psychologists, athletic trainers, and massage therapists; yet they appear to pay inadequate attention to a deceptively important aspect of success.

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Endurance sports have historically been at the forefront of data analysis.

Scientific research on energy production and the causes of fatigue inform coaches’ training plans. And of course, coaches collect copious data on pacing from both races and practices. Our understanding of pacing strategy is imperfect, but what we do know suggests that relatively even pacing is better than dramatically uneven pacing (Abbiss et al, 2006; Foster et al, 2008; Gosztyla, 2006; van Schenau et al, 1994). Studies of recordsetting performances on the track show that in events longer than 800m, pacing strategy is characterized by a fast start, slower middle, and a fast finish (Tucker et al, 2006). Physiology supports the analytical conclusion that in distance events, relatively even pacing yields more successful results than uneven.

Yet, cross-country results illustrate an inability for runners to apply the lessons that research teaches.

There are several significant obstacles to understanding cross-country pacing.

First, terrain varies. To use an extreme example, if a course goes straight uphill for the first half, and straight downhill for the second, even splits would not be optimal. And a course with tight bottlenecks might require getting through the bottleneck first, even if doing so causes dramatic slowing later in the race.

(Although, the success of such a strategy hinges on one’s ability to get through the bottleneck ahead of all the other runners trying to get there first.) Secondly, course measurements are imprecise – neither USATF nor IAAF will certify cross-country course distances. Third, coaches may intentionally instruct athletes to race in ways that aren’t conducive to achieving the fastest times – perhaps most commonly, employing pack-racing tactics which result in non-optimal pacing for both the fastest and slowest members of the pack.

Despite these obstacles, cross-country racing is ripe for analysis. Part one of this paper details the statistical analyses used, and demonstrates the degree to which pacing correlates with finishing time. Part two addresses the potential impact of better pacing on performance.

Part three concludes with a discussion of possible reasons why proper pacing in cross-country appears to be so dif-

ficult to achieve. It is important to stress that this is a preliminary investigation.

The statistical analysis is rudimentary, and there is much we do not know and cannot yet control for. As such, this is the beginning of a conversation. Finding ways to improve pacing strategy should be a significant training goal for any cross-country runner – indeed, better pacing is essentially free improvement: without increasing speed or fitness, athletes will run faster, and teams will place higher.

PART ONE – STATISTICAL ANALYSIS Results from 12 NCAA cross-country races were analyzed. Races were selected on the following criteria: Field size greater than 150; race distance of 10,000m and provision of 2000m splits for all athletes; and regional or national championship races (under the assumption that athletes and coaches are more likely to be racing to optimize finishing time and place at championships than during the regular season). These criteria limited the selection of races to men’s NCAA Division I or II Regional or National Championship races. Five National Championship races and seven Regional Championships were used in the analysis. (Table 1 lists the twelve meets analyzed).

Each athlete’s final race pace was expressed as a percentage of their 2K split pace (Final RP / 2K Pace). For example, if Final RP = 102.53% of 2K Pace, the athlete’s final overall RP was 2.53% slower than their pace early in the race; if Final RP = 98% of 2K Pace, the athlete actually got faster throughout the race. We can then look at the correlation between this number and finishing time.

Graphs 1 & 2 show a very clear correlation: for each quintile, pacing grows progressively more positive. The top quintile of finishers had the least positive splits, the 2nd quintile paced more positively, down to the last quintile, which used the most positive pacing.

Table 1 shows a more precise look at this relationship. These results suggest very strong correlations and predictive powers (all p values < .0001, adjusted R2 values ranging from 38.9% to 82.4%), with predicted finishing time improving between 18.6 and 31.7 seconds for each 1% improvement in Final RP / 2K Pace,

depending on the race.

As a second step in expressing the relationship between pacing and finish time, a virtual race model was created using data from the 12 races. First, finish results from each race were collapsed into 100 data points by taking the average finishing time for each percentile in each race. Then, for each percentile, the average pacing strategy (expressed as Final RP / 2K Pace of the runners in each percentile) was calculated in each race.

Finally, these finishing times and pacing strategies were averaged across all 12 races. Essentially the model turns the thousands of runners from the 12 races into 100 virtual “runners” – with each runner’s finish time and pacing strategy calculated by averaging the respective percentile results from each race: that

is, the finishing time and pacing strategy of the first virtual runner is the average of the finishing times and pacing strategies of the top one percent of finishers in each race, and the 2nd place virtual runner’s time and pacing is the average of the times and paces of the second percent of finishers, all the way down to 100th place and the last percent. Graph 3 shows the relationship between pacing and finish time for this model. It is remarkably strong: R2 = .96. The fastest virtual finishers pace closest to even, and finishing time and pacing progress in a remarkably linear fashion down to the slowest (and most positive-pacing) runners. Granted, turning the multitudes of runners from 12 races into 100 data points has the effect of smoothing out a lot of statistical variation; even so, this

is a surprisingly close linear relationship between pacing and finishing time.

To summarize most simply, the top runners are the ones who slow down the least, while rear-pack runners start too fast and slow too much. In some cases (this appears to be one), statistical analysis merely restates the obvious. Although it may be uncharitable to say so, the news that college cross-country runners employ sub-optimal pacing is not earth-shaking. However, the stark truth of this analysis leads to an important and deceptively difficult question: Why do so many athletes employ such sub-optimal pacing? There is no rational reason this should be, no reason why runners with PRs of 33 minutes should pace differently than 30-minute runners. If runner A has a PR 10% slower than runner B, sensible strategy dictates that runner A run the first 2K roughly 10% slower than runner B. Why does runner A race as if his PR is much closer to that of runner B? Several caveats: First, variables besides pacing exert much greater influence on finishing time – most obviously, running ability. It is fair to assume that the top finishers are more talented than mid- and rear-pack finishers. Pacing is not the causative factor – a 33-minute 10K runner is not going to beat a 30-minute 10K runner just by pacing better.

But because there is no obvious way to control for talent, this analysis does not account for running ability – thus, pacing appears to be a stronger predictor than it is. However, it is worth stating again: there is no reason why the optimal pacing strategy for a 33-minute runner should be different from that of a 30-minute runner. Their pacing profiles should be similar, merely adjusted for speed. Secondly, some races (most egregiously, the 2014 Great Lakes Regional) are essentially sit-and-kick affairs with very conservative early paces. In these cases, pacing was probably sub-optimal; however, even eliminating sit-and-kick races, there is still a clear correlation between less-positive pacing and faster times. Further, in cases where we know the final 2K split, overall pacing is much more predictive of final time than finishing speed (time of the last 2K relative to overall race pace).

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those in slower quintiles) have less wiggle room – they would need to run relatively closer to their actual 2K split in order to finish higher. Still, on average, those in the 2nd quintile could run the first 2K 7.4 seconds slower, and still run a faster time if they used a better pacing strategy. (On the track, this would be 1.5 seconds per lap, a significant deviation from prescribed pace for elite runners.) For the third and fourth quintiles, their first 2Ks could be 10.8 and 14.7 seconds slower respectively, while for the last quintile, the first 2K could be a remarkable 23.7 seconds slower. In other words, many athletes could probably rein in their early pace quite significantly and still finish in a better spot.

PART THREE – WHY IS PACING IN RACE CONDITIONS SO DIFFICULT?

PART TWO – SHOULD RUNNERS START SLOWER? The above analysis is not enough to show that runners could finish significantly faster simply by pacing better. Assuming finishing time reflects near-maximal effort, runners cannot be expected to go out at the same pace and finish faster. To pace more evenly, a runner must start slower. We may not know how much slower a runner should start – this depends on how far over the redline his early pace was. However, we can say how much slower he could start and still run faster if his pacing were better. For this analysis, successful pacing strategy is defined as the average 12

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pacing strategy (Final RP / 2K Pace) of the top quintile of finishers in a race.

Then, we can ask the following question: if runners outside of the top quintile adopted the successful pacing strategy (“Top Quintile’s Final RP/2K”), how much more slowly could they have run the first 2K and still finish at least one second faster than their actual time?

This newer, slower 2K split is found by the following equation: New 2K Split = (Actual Finishing Time – 1)/(5*Top 〖Quintile〗^’ s (Final RP)/2Kpace) Table 2 gives the results by quintile for each race. Runners in the faster quintiles (who tend to pace better than

Pacing is not easy. Decades of coaching observations at meets of all levels support this conclusion. Whatever mechanism in the body is responsible for regulating pace, humans are not naturally good at it in race conditions.

Yet, athletes in organized training programs spend significant amounts of time training at particular paces – including race pace. Assuming athletes are able to pace themselves relatively successfully in weekly interval workouts, something must change in races.

Several possibilities suggest themselves. First, team scoring exigencies may lead coaches to employ sub-optimal pacing in pursuit of lower scores.

Coaches may instruct athletes to “run up” – for example, have runners 4 and 5 stick with #3 for as long as possible.

Or, coaches may exhort athletes to “get out” early in the race, to the detriment of athletes’ ability to maintain that pace throughout. But we are speculating. We do not have data on how many athletes were instructed to get out quickly in a given race, nor do we know whether those are the athletes falling off the pace. Possibly, these strategies are sensible gambles, but if

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everyone in a race is attempting to get out, most are getting in over their heads without improving their position relative to the field. An overly aggressive start won’t work if everyone uses it.

A second possibility is that athletes need earlier external feedback to support their internal pacing cues. On the track, athletes get pacing information no later than 400m – or 4% – into a 10K race. In cross-country, the first data point may come 10% (1K), or 17% (1 mile) into the race – at which point it may be too late to correct. Athletes may need information earlier in cross-country. (Although, it is precisely during this first part of the race that athletes are most often told to get out quickly.)

Finally, the useful information runners receive may be overwhelmed by more powerful external signals. Noakes and others postulate that the central nervous system plays a significant role in regulating exertion in endurance events (Tucker and Noakes, 2009); perhaps, under competitive stress, runners are less able to heed messages from this regulating system. Here, the likely culprit is the field itself, as internal pacing cues are overwhelmed by signals from hundreds of fellow competitors. A herd mentality takes over. Herding, well-studied in the social and cognitive sciences, is defined 14

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as “the alignment of thoughts or behaviors of individuals in a group (herd) through local interaction, and without centralized coordination.” (Raafat et al, 2009.) It is often an unconscious emotional reaction – individuals respond without realizing that they are doing so.

Self-awareness is dramatically impacted by what one researcher describes as an “emotional contagion” (Raafat et al, 2009), which could well interfere with internal regulators. Thus, lacking definitive information either from the coach or the stopwatch, runners rely on the herd, which reinforces an overly emotional and aggressive approach in the early stages of the race. No matter the pacing habits inscribed in practice, the herd drives the runners to a pace they cannot maintain. Eventually, physiological reality overcomes the herding impulse, and early exuberance melts in the face of fatigue. The runner slows down, and is dropped from the herd.

The practical implications of herd mentality for distance running, though well-understood in a general way by coaches, probably would yield greater insight if subjected to greater scientific study. Herding is not entirely negative. A “safe” pace – what the body might naturally choose in isolation – likely minimizes risk of damage to the body, but it

is probably not fast enough to maximize competitive potential. Success in competitive distance running probably requires overriding (to some degree) internal regulators. There is evidence that competition – both against oneself and against others – can lead to “supramaximal” performance. (Wilmore, 1968). It remains to the coach to find the appropriate balance between using the power of the herd and not surrendering completely to its siren call.

As noted at the top, the analyses presented in this article are preliminary. There are many areas in need of further investigation: how coaches teach pacing in practice, and what instruction is given at races; which athletes set the pace in a crowded field, and how a crowded field impacts runners’ exertion and pacing; how fields impede runners who start conservatively and attempt to move up; the degree to which cross-country pacing differs from that in track and road races; whether women pace differently from men – all of these may yield deeper insight. Successful crosscountry pacing is at once both simple, and difficult to define: To paraphrase Justice Potter Stewart’s threshold for obscenity, we know good pacing when we see it. Unfortunately, we also too readily accept bad pacing from our athletes. A better understanding of what good pacing is – and how to coach it in athletes – will help coaches and athletes employ better pacing and enjoy better results.

SOURCES http://www.basketball-reference. com/leagues/NBA_2001.html, and http://www.basketball-reference.com/ leagues/NBA_2016.html#all_team_ stats.

Abbiss, C., & Laursen, P., (2008).

Describing and understanding pacing strategies during athletic competition, Sports Medicine, 38(3), 239-252.

Foster, C., Snyder, A., Thompson, N., Green, M. Foley, M. & Schrager, M., (1993). Effect of pacing strategy on cycle time trial performance, Medicine and Science in Sports Exercise, 25(3),

383-388.

Gosztyla, A., Edwards, D,, Quinn, T., & Kenefick, R., (2006). The impact of different pacing strategies on five kilometer running time trial performance, Journal of Strength and Conditioning Research, 20(4), 882-886.

Raafat, R., Chater, N., & Frith, C., (2009). Herding in humans, Trends in Cognitive Sciences, 13(10), 420-428.

Romer, D., (2006). Do firms maximize?

Evidence from professional football, Journal of Political Economy, 114(2), 340365.

Tucker, R., Lambert, M., & Noakes, T., (2006). An analysis of pacing strategies during men’s world-record performances in track athletics, International Journal of Sports Physiology and Performance, 1(3), 233-245.

Tucker, R., & Noakes, T. (2009). The physiological regulation of pacing strategy during exercise: a critical review, British Journal of Sports Medicine, 43(1), 1-9. van Schenau, G. de Koning, J., & de Groot, G. (1994). Optimisation of sprinting performance in running, cycling and speed skating, Sports Medicine, 17(4),

259-275.

Wilmore, J. (1968). Influence of motivation on physical work capacity and performance, Journal of Applied Physiology, 24(4), 459-463.

Sources for meet results http://www.flashresults.com/2010_Meets/ xc/NCAAMWRegion/ http://www.gosycamores.com/ fls/15200//statistics/cross/MenIndividual. htm?DB_OEM_ID=15200 http://www.deltatiming.com/results/ xcresults.aspx?yf=2011&mf=2011-ncaad1-great-lakes-region-xc&ev=1&sp=True http://www.ustfccca.org/assets/ results/2011xc/ncaa-d1-xc-2011-MEN. html http://www.deltatiming.com/results/ xcresults.aspx?yf=2012&mf=2012-ncaaxc-south-regional&ev=2&sp=True http://results.deltatiming.com/xc/2012ncaa-d1-cross-country-championships/ results/2 http://pttiming.com/events/87928395 http://www.onlineraceresults.com/ race/view_plain_text.php?race_id=37193, https://www.tfrrs.org/results/xc/9178. html#76508

http://flashresults.com/2016_Meets/xc/ NCAASE/index.htm http://results.flotrack.org/2015/11-13NCAAD1S/Web/Timetable.php?D=1 http://branchsportstech.com/2015_ Meets/XCView/# http://www.ncaa.com/ncaa-cross-country-championship-live-timing

Steve Lane has been the cross-country and track and field coach at Concord-Carlisle High School since 1998. His teams have won seven MIAA titles, 13 Dual County League titles, and the Concord-Carlisle boys cross-country team was named “Team of the Decade” for 20002009 by the Mike Mahon XC Poll. He has twice been named Boston Globe Coach of the Year.

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The Long Jump Physical Backgrounds of the long jump for Coaches and Athletes

By Vladimir Strelnitski, Max Coleman, and Bill Ferguson

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THE LONG JUMP

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n this article addressed to practicing coaches and athletes we present a summary of physical phenomena involved with the long jump. We explain the mechanics of the long jump on the basis of the horizontal and vertical components of the athlete’s center of mass velocity. We believe this approach presents the processes more clearly than the approach based on the total velocity and the projection angle, which has lately been used in most of the publications on the subject.

Introduction An abundant literature concerning the physical aspects of the long jump exists, but most of these research papers are written in a language that intimidates practicing coaches and athletes. Yet, there is little doubt that the understanding of the physical background of the sport a coach teaches makes the training process more efficient. This article is a result of numerous mutually beneficial discussions of the physics and techniques of the long jump by two coaches (M.C. and B.F.) and a physicist and coach (V.S.). We tried to follow the famous Einstein’s remark and present things as simply as possible but not simpler.

In section 2, we formulate five questions about the technique of the long jump requiring some knowledge of physics to be answered correctly. Section 3 summarizes the concepts of physics that help answer these questions. In Sections 4 and 5 we use these concepts to explain the key elements of the long jump technique; the concept of velocity is central for section 4 and the concept of force – for Section 5. In Section 6 we elaborate on the important issue of the jump leg flexure during the board contact phase and emphasize the gradual character of the increase of the takeoff knee angle and of the leg stiffness with the increasing mastership of the athlete (increasing takeoff speed). Section 7 summarizes our conclusions in the form of brief answers to the five questions of Section 2.

We hope that the article will be helpful to coaches and athletes working on improvements of the long jump technique and interested in knowing not only what should be done for a good jump but also why it should be done.

Five questions that require physics to be answered 18

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(1) Why should the run up for the long jump be as fast as possible?

(2) Should the jumper intentionally flex and then straighten the knee joint of the jump leg during the foot contact with the board – in order to jump higher?

(3) What is the purpose of launching the arms and the free leg up after the contact of the jump foot with the jump board (“touchdown”)?

(4) Can the jumper increase the height of the free flight (and thus the time and distance of the flight) by applying an appropriate technique during the flight (such as hitch-kick)?

(5) Why should the jumper stretch the legs forward before landing?

Physical concepts to know To understand the basic physics of the long jump one should be acquainted, as a minimum, with (1) the notion of the center of mass (COM); (2) the three Newton’s laws of classical mechanics; and (3) the “vector” nature of velocity and force.

3.1. The center of mass (COM) Slightly simplifying the strict definition, one can define COM as the point relative to which all the weights of the body’s parts are mutually balanced. In other words, if the body is suspended on a string attached at the COM, it will hang in equilibrium.

3.2. Newton’s laws First Law (The law of inertia): An object stays in motion with the same velocity unless it is acted upon by an external force.

Second Law (The force-acceleration law): A force acting upon an object produces acceleration (change of the numerical value or the direction of the velocity of the motion).

Third Law (The action-reaction law): Two interacting bodies always act upon each other with equal opposite forces.

3.3. Vectors The velocities and forces involved in the long jump are “vectors” – physical quantities that are characterized not only by their numerical value but also by their direction.

Graphically, a vector quantity is represented by an arrow showing the direction, the length of the arrow giving, in some units, the numerical value of the physical quantity.

An important property of vectors is the way they add to each other: The sum of two vectors is the diagonal of a parallelogram whose sides are the added vectors.

If the added vectors are perpendicular to each other (for example, one is horizontal

and the other is vertical), the parallelogram is reduced to a rectangle. From the “parallelogram rule” of vector addition it follows that any vector can be “resolved” into two components acting along two arbitrary directions. For example, the velocity V of the jumper’s COM can be resolved into its horizontal (Vh) and vertical (Vv) components (Fig. 1, pg. 20). The choice of the horizontal and the vertical directions for the two components is handy in our case, because the length of the long jump is measured horizontally, and the force of gravity with which the jumper struggles to jump farther is vertical. Fig. 2a (pg. 20) gives another example – the horizontal and vertical components of the force F exerted by the jump foot on the takeoff board during the contact phase, while Fig. 2b (pg.

20) shows the reaction force of the board R (Newton’s 3rd law!) and its horizontal Rh and vertical Rv components.

The combination of the two components of a vector is totally equivalent to the parent vector. For example, the motion of the COM at takeoff will be the same if instead of considering its moving with the velocity V we consider it to participate in two independent motions – horizontally, with velocity Vh and vertically, with velocity Vv. Similarly, the mechanical effect of the force R in Fig. 2b would be the same if, instead of this force, two independent forces, Rh and Rv, were applied simultaneously.

In the following sections, we will show how these physical concepts help us understand the techniques of the long jump.

The length of the jump The long jump is a Track and Field event in which the participants are supposed to demonstrate how far they can leap forward in the horizontal direction taking off before a “foul line” drawn on the ground perpendicular to the direction of the jump. The “legal” length of the jump is the sum of three horizontal distances: (1) the distance Dt between the foul line and the COM of the jumper at the instant of takeoff (in a correct jump, the COM at this instant is always in front of the takeoff line), (2) the distance Dl between the heels of the jumper and the COM at the instant of landing (in a correct jump the heels land in front of the COM and leave the most distant mark in the sand), and (3) the distance Df that the COM covers during the free flight. The first two distances are “subsidiary.” The jumpers, of course, should try to maximize

THE LONG JUMP

Fig. 1. Resolving the takeoff velocity V of the jumper’s COM into its horizontal V and vertical V components using the “parallelogram rule.” h

v

Figure 2. (a) – The force F of the jump foot pressure on the takeoff board and its horizontal (Fh) and vertical (Fv) components soon after touchdown. (b) – The reaction force R exerted by the board on the jump foot (according to the Newton’s 3rd law) and its horizontal and vertical components, Rh and Rv , which, respectively, decelerate the horizontal motion and accelerate the upward vertical motion of the jumper.

them, but in the jumps of the elite athletes these distances only make 10-15% of the total legal distance, the major part being acquired in the free flight.

The motion of the COM in the free flight is described by the ballistic equation. The original form of this equation, which we will be using here, gives the horizontal length Df of the parabolic trajectory of the COM as a function of its horizontal Vh and vertical Vv speeds at the takeoff and the difference h of its height at the moment of takeoff and the moment of landing (h is considered to be positive when the COM is lower at the landing than at the takeoff, as it should be in a good jump): 20

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where g ≈ 9.8 m/s2 is the acceleration of gravity. The equation looks cumbersome because of the term within the braces, which, actually, is much less important than the pre-brace term.

The pre-brace term shows that the free flight length Df depends on the product of the horizontal and vertical speeds at the moment of takeoff. It would be great if there were a technique that would allow the jumpers to attain simultaneously their personal records in Vh and Vv. Unfortunately, such a technique

is unknown. In any real technique, an increase in the vertical speed is only possible at the expense of a decrease in horizontal speed, and therefore the jumper has to look for a compromise that would not necessarily maximize each of these velocity components but would maximize their product.

The major characteristic feature of the running long jump is a drastic difference between the maximum possible vertical speed and maximum possible horizontal speed at takeoff: the latter is more than twice as great as the former. Let us see why.

The horizontal speed is limited by the highest possible speed of the run-up sprint, which is currently around 11 m/s.

Because of the unavoidable loss of the horizontal speed due to the braking interaction of the jump leg with the ground between touchdown and takeoff (see below), one can admit 10 m/s as a realistic upper limit for the horizontal takeoff speed.

The upper limit for the vertical takeoff speed can be estimated by the maximum height H to which the jumper can lift his or her COM after takeoff. The equation describing the connection between Vv and H is:

The elite male high jumpers are capable of lifting their COM by about 1.0 m from its takeoff height, which, according to the above equation, corresponds to the initial vertical speed of approximately 4.4 m/s.

Such an extreme vertical speed is obviously unachievable for a long jumper because of technical limitations; one can take 4.0 m/s as a “very optimistic” upper limit.

With these upper limits for Vh and Vv and with the COM takeoff to landing drop of 0.6 m (achievable by the best jumpers), equation (1) gives the maximum theoretical range of the free flight part of the jump: Df ≈9.5 m. The horizontal shifts between the COM and the feet at the moments of takeoff and landing (Dt and Dl) can add together up to ≈1m to the legal distance D, which results in D≤10.5 m. This can be compared with the current long jump world record of 8.95 m.

The stricter limitation for the vertical takeoff speed than for the horizontal speed allows for understanding of why the projection angle (Fig. 1) in the high-class long jumps is much smaller than the well

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THE LONG JUMP known optimal angle for a simple projectile, the famous “45°.“The projection angle a is determined by the ratio of the vertical and horizontal component of the velocity (Fig. 1). In trigonometry, this ratio is called the tangent (tan) of the angle. So, a is the angle whose tangent equals Vv/Vh. Using the maximum possible values for the speeds, Vv ≈ 4 m/s and Vh ≈ 10 m/s, we find: tan a = Vv /Vh ≈0.40, and any modern calculator (e.g. the one in your iPhone) will tell you that the angle having this value of tangent is a ≈22°. And, indeed, the average projection angle for elite long jumpers, both men and women, is a ≈21° (Linthorne 2007).

The less important, braced term in equation (1) describes the correction to Df due to the lowering of the COM at the instant of landing relative to its height at the instant of takeoff. A 1.8 m tall male jumper can drop his COM by h ≈0.6 m. Equation (1) shows then that for the 8-m range jumps (Vv ≈3.5 m/s , see below) this lowering of the COM increases the distance of the flight Df by ≈ 20%. It is a considerable improvement, and one of the reasons why the jumper should stretch the legs forward before landing (the second reason being, obviously, the increase of the distance Dl). It is interesting that for a less experienced athlete, for whom the vertical takeoff speed is lower, the same drop of the COM at the landing would increase the distance of the flight more efficiently.

For example, for the 6 m range jumps (Vv≈2 m/s ) the increase of the distance due to the same (0.6 m) drop of the COM is already≈50%. Still, the jumpers must work on increasing both their horizontal and their vertical takeoff speed, because the pre-brace term in equation (1) is more important in creating the length of the jump than the braced term Note that it is the COM of the body what moves along the ballistic parabola in free flight. The position of the COM is not fixed in the body, it changes when the relative positions of the body’s parts change. Thus, what moves exactly along the parabola is not a fixed part of the body (like the pelvis, for example) but a mathematical point migrating in (or out) of the body in accordance with the momentary relative positions of the body’s parts. The position of the COM at each instant can be calculated if the masses and spatial positions of all the parts of the body are known. We emphasize that after the takeoff and up to the landing no relative movements of the 22

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parts of the body can change the free flight trajectory of the COM. In particular, no leg or arm technique during the free flight can make the COM fly higher, and thus increase the length of the jump. All the leg, arm, torso, and head movements in the flight are only aimed at creating the optimum body position for landing.

The creation of the vertical speed It is important to understand that the vertical speed in the long jump is due exclusively to the reaction force developed in the takeoff board in response to the pressure from the jump foot. More precisely, the upward acceleration is due to the vertical component of this reaction force (Fig. 2b).

What should the jumper do to create and maximize this reaction force?

An obvious answer seems to be: To use the “spring” of the jump leg by intentionally bending and then powerfully extending it to produce a strong pressure on the board and get the correspondingly strong reaction response. Don’t we do just this (with both legs) in the standing long jump?

Well, in the high-class running long jump, there is simply not enough time for that! The time of the jumper’s contact with the board can be estimated as the time the COM moves from its position (behind the jump foot) at touchdown to its position (in front of the jump foot) at takeoff – a distance of about 1m. The horizontal speed of the motion is ≈10 m/s, and thus the time of the contact is only on the order of 1/10 of a second! The “spring“ of the leg that would first deeply bend and then fully extend would be too “soft” to accomplish its work during such a short time.

Here is a simple example that may help understand this. Imagine a ball falling vertically on a horizontal platform that is available only for a small fraction of a second after the contact with the ball, after which it is swiftly removed downward. If the ball is soft, it will not have enough time even to compress in full after the moment of contact. Before it fully compresses and bounces off, the platform is already gone, and the ball will keep moving downward, instead of rebounding up or, at best, it will rebound with a small upward speed. In order for the ball to rebound with full efficiency in the available short time of contact, it must be stiff enough, which means that a small deformation creates a strong reaction force in its material. It is clear,

intuitively, that the smaller the deformation necessary for the full rebound, the shorter the needed time of contact. [An interested reader can find the details of the bouncing ball physics in Cross (1998), where it is shown, in particular, that the time of the ball contact with the surface decreases with the increase of the ball’s stiffness.]

This brings us to the technical solution that is used by experienced long jumpers. They do not bend their jump leg after the touchdown, at least they do not bend it intentionally. On the contrary, they do their best to resist the increase of the knee flexion at the touch down, making the leg as stiff as possible on the last stride before the touchdown. The knee joint will bend a little after the touchdown, because of the very strong shock, but the jumpers try to minimize the flexure. As a result, the intentionally stiffed leg behaves like a very stiff spring. This spring absorbs the board’s reaction force quickly and quickly transmits it to the whole body.

The movement of the COM upwards starts immediately after the touchdown.

The COM lifts even while the knee of the jump leg is still (involuntarily) flexing. The lifting is due to the pivoting of the body around the jump foot (“stopped” by the board) and by throwing the arms and the free leg up. Throwing these parts of the body up increases the vertical pressure of the jump foot on the board. This effect of “kick back” is the consequence of the second and third Newton’s laws and can be easily verified with an ordinary bathroom scale. Stand on the scale with your two feet and notice your weight. Then throw your arms and/or one leg up: you will see the scale showing a considerable instantaneous “increase of weight” (increase of the pressure on the scale) and then a quick return back to the normal value. The increase of the jump foot pressure on the board produces an additional upward reaction force from the board.

After the moment of maximum knee flexion, the forcefully extended muscles of the jump leg thigh react by a fast “concentric contraction” which extends the jump leg increasing the pressure on the board (and thus the vertical reaction force of the board) even more. Finally, a small addition to the pressure on the board (and thus to the upward reaction force) is produced by the extension of the ankle joint before the takeoff. In the high-class jumps, the combination of these actions imparts to

THE LONG JUMP the COM an upward velocity as high as ≈ 3.5 m/s by the moment of takeoff.

Contrary to the vertical component of the board reaction force, the horizontal component (Fig. 2b) plays a negative role – it brakes the horizontal speed up to the instant the COM of the body pivoting around the jump foot has moved forward to the position just above the foot. After this moment, the jump foot presses the board backward, and the opposite reaction force gives the athlete some impulse forward, compensating a little for the loss of the horizontal speed in the first phase.

This compensation is not complete: typically, by the moment of takeoff, 15-20% of the touchdown horizontal speed has been lost. Thus, the touchdown-to-takeoff phase allows the jumper to create the maximum possible upward speed at the expense of a 15-20% loss in the horizontal speed.

The technical growth The above discussion concerned the physical backgrounds of the high-class long jumps. Here we focus on some tendencies in the technical growth of non-elite athletes.

Two typical differences of a beginning long jumper from an elite jumper are: (1) a lower run-up speed and (2) weaker muscles and joints of the legs.

A lower run-up speed (Vru) entails a longer foot contact time (takeoff duration).

An experiment, in which an elite athlete was asked to jump full force but with various lengths (and thus various speeds) of the approach, demonstrated a decrease of the takeoff duration from ≈0.20 s to ≈0.12 s when the run-up speed increased from ≈5 m/s to ≈11 m/s (Linthorne 2007). The experiment showed that the athlete tried to jump as high as possible, regardless the speed of approach – to achieve the maximum length of the jump by increasing the time of the free flight. So, the vertical takeoff speed remained almost constant, close to the maximum for this athlete, whereas the horizontal takeoff speed grew almost proportionally to the run-up speed. As a result, the projection angle of the jump (determined by the ratio of the vertical and horizontal takeoff speeds) was gradually decreasing, from ≈40° at Vru ≈5 m/s to ≈20° at Vru ≈10 m/s.

Another important result from this experiment was the observed decrease of the touchdown knee angle with the decreasing run-up speed – from ≈170° at Vru ≈ 11 m/s to ≈150° at Vru ≈ 5 m/s. As 24

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discussed above, the touchdown shock in the high-class (fast run-up) long jumps produces unintentional flexure of the knee joint. But since the intensity of the shock decreases with the decreasing run-up speed, the observed increase of the knee flexion could only be intentional. By flexing and powerfully straightening the leg, the jumper increases the foot pressure on the jump board and achieves the maximum possible upward speed, compensating for the lower efficiency of the “pivoting” acceleration mechanism at lower runup speeds. Using the muscles of the jump leg intentionally for an additional push on the jump board becomes possible because of the longer takeoff duration when the run-up speed is lower.

Supplementing these results with the standing long jump, where the “pivoting” mechanism of upward acceleration is totally absent, and both the vertical and the horizontal accelerations are obtained only by an intentional flexing/straightening of the legs, one can come to the conclusion that from the standing long jump through the running jumps with growing run-up speeds, to the highest run-up speed jumps, the role of the intentionally used muscles of the jump leg to create the maximum upward velocity gradually decreases, whereas the role of the “pivoting” mechanism on a pre-activated leg gradually increases. Therefore, when working with inexperienced long jumpers the coach should make efforts to gradually attain: (1) an increase of the approach speed, (2) an increase of the leg muscle strength, and (3) a decrease of the intentional knee flexure at the touchdown. The last two requirements aim at making the “spring” of the jump leg as stiff as possible for high-class jumps.

board available in a high-class (high runup speed) jump. At the touchdown, the jump leg must be as stiff as possible and its foot must be put on the board flat to avoid a premature ankle joint flexion.

(3) Launching the arm(s) and the free leg upward upon the touchdown has a triple effect: it increases the pressure of the jump foot on the board (and thus the upward reaction of the board); it raises the takeoff height of the COM (and thus the length of the free flight); and it helps to keep the torso vertical by opposing the forward rotation caused by the stopping of the jump foot by the ground at the touchdown.

(4) After the takeoff, no relative movements of the body parts can alter the motion of the COM along the ballistic trajectory whose parameters (including the maximum height of the flight) are predetermined by the horizontal and vertical velocities of the COM at the instant of takeoff.

(5) Before landing, the jumper stretches the legs forward in order to (i) maximally lower the COM at the moment of landing and (ii) maximally increase the horizontal distance between the feet and the COM at landing – both effects increasing the distance of the jump.

References Cross, R. C. (1999, The bounce of a ball, Am. J. Phys. 67, 222-227. http://www.physics.usyd.edu.au/~cross/ PUBLICATIONS/BallBounce.pdf Linthorne, N.P. (2007). Biomechanics of the long jump. In Routledge Handbook of Biomechanics and Human Movement Science, Y. Hong and R. Bartlett (Editors), Routledge, London. pp. 340–353. https://www.brunel.ac.uk/~spstnpl/ Publications/Ch24LongJump(Linthorne). pdf]

Conclusions We summarize our conclusions by briefly answering the five questions formulated in Section 1: (1) Although the relation between the horizontal and vertical takeoff velocity components is complex, the experience shows that an increase in the run-up speed increases the product of the two velocity components, to which the distance of the jump is proportional [equation (1)].

(2) Flexing and straightening the knee of the jump leg intentionally softens the “spring” of the leg. This increases the time of the leg re-bouncing beyond the very short time of the foot contact with the

Vladimir Strelnitski has PhD in astrophysics. Director Emeritus of the Maria Mitchell Observatory (retired in 2013). He taught the Physics of Movement at Springfield College in Massachusetts and currently coaches the hammer throw at Springfield.

Max Coleman is the sprints, jumps and hurdles coach at NCAA Division II Catawba College in Salisbury North Carolina.

William Ferguson is Currently Assistant Coach for Track and Field / Cross Country at Moravian College.

How Teams Fail Is Your Program Headed Towards Destruction?

Marques R. Dexter, Ashley Fallaize, Nicole McCluney, Dr. Paul Schempp

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HOW TEAMS FAIL

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o one likes to be on a losing team. Yet, many programs struggle to overcome continual defeat. Those stuck in dysfunctional and deflated programs are often unaware of the causes of their demise. This article identifies factors accounting for athletic team failure and offers suggestions to reverse the trend and stimulate success.

Domineering Personalities Athletes are more likely to buy into a program led by a coach whose personality empowers rather than intimidates them. Unsuccessful programs are commonly led by coaches with domineering personalities who emphasize winning over the athletes’ holistic growth (Vallee & Bloom, 2005). These coaches often experience stagnation in their professional development and believe they hold the answers to future success with no responsibility for current failures. Consequently, they feel they have all the knowledge they need, fail to seek growth opportunities, and see only those above them as capable of providing any relevant information.

Furthermore, coaches with this mindset rarely engage in opportunities to self-reflect, ultimately leaving them to perpetuate the same mistakes they’ve made in the past.

Empowering environments are a hallmark of traditionally successful programs. Within these programs, all members are shown respect regardless of their ability or position. This culture enables athletes to emulate the attitudes and values that permit everyone to thrive both competitively and personally. Everyone’s voice is valued, promoting an increased investment in the team’s success by all stakeholders. And it all begins with the coach.

To the members of your program, who you are as a person matters more than what you know as a coach.

Totalitarian Leadership Intimidating leadership styles, like domineering personalities, are detrimental to athlete development (Valleé & Bloom, 2005). Coaches demonstrating these styles are often found to be less confident in their abilities, convey contradictory messages, and display inconsistent, volatile behavior. The 28

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oppressive atmosphere created by totalitarian leaders is not conducive for athletes and personnel to perform their best. Ultimately, the suffocating and hostile environment leads to personnel turnover, ineffective recruiting, and a negative program reputation.

Leaders of successful programs promote healthy, holistic athlete growth.

They do so by maintaining composure in tough situations, keeping an open mind to change, and promoting a standard of positive interaction throughout the program. These coaches encourage a more democratic environment where ideas and individuals are safe, respected, and appreciated. Programs led by democratic leaders foster stronger commitment because the members feel greater autonomy and ownership in the organization.

Prizes Over People When an athlete’s personal growth and independence is overshadowed by a coach who sees athletes solely as performers, team failure is eminent.

In such situations, the athlete’s worth is singularly measured by their contributions to the team’s trophy case and, subsequently, the coach’s glory.

Programs rooted in this philosophy produce athletes who seldom develop outside their athletic identity and ultimately lack appreciation for who they are as people. Similarly, the staff feels unappreciated for who they are because they are valued only for what they do. Moreover, the support staff is considered just that—support, and nothing more. Production is valued over personal worth and dignity. This win at all cost philosophy permeates everything and everybody in these dysfunctional programs.

In contrast, within successful programs, coaches aspire to equip athletes with skills, strategies, behaviors, and values to build champions both in and out of the sporting arena. Successful programs not only develop high-performing athletes, but purposely use the sporting experience to build confident, productive, and engaged members of society. To accomplish this, developing life skills and a sense of empowerment is an integral aspect of the program’s culture. Empowerment consists of encouraging and valuing athletes’

independence, beliefs, aspirations, individuality, and personal growth.

Coaches convey these tenants by emphasizing emotional control, academic achievement, and the pursuit of excellence on and off the field. The values of honesty, open communication, respect, and trust underpin the interactions of all team members. Developing well-rounded individuals to be successful in life long after their playing days come to an end is a paramount priority of programs with traditions of success.

Destructively Disorganized Empty vans, forgotten equipment, practices without players, lack of meal money, delinquent requests and reports, communication breakdowns, these are all too often found in teams that are destructively disorganized.

Continual disarray causes stress and frustration, leaving athletes and staff more confused than confident. Two common causes of this systematic disarray are a) a lack of planning and b) established routines. Without planning to guide future actions or established routines to systematize everyday activities, team members are clueless as to what to do, when to do it, or how to do it. Chaos reigns supreme.

It is particularly common for inexperienced coaches to become overwhelmed by the mountain of duties and obligations demanded of them.

Typical behaviors of disorganized coaches include: a) failure to thoroughly plan practices, b) neglecting to include or provide adequate athlete recovery time, c) being unprepared for multidimensional demands of competition, d) limiting their plans with short-term objectives and e) failure to adequately undertake their leadership and administrative responsibilities.

Coaches of successful teams spend extensive time planning practices tailored for athlete development and success. During practice, coaches and athletes are mutually engaged in activities to produce more favorable results during competitions. Carefully crafted routines provide regularly structured patterns of behavior to accommodate the everyday mundane tasks of team operation. For example, routines are established for athletes

coming into the locker room, opening practice, preparing for a competition, receiving news from coaches, and other vital aspects that keep a team running smoothly. By incorporating these habits into their daily routines, coupled with the adoption of the personality traits and attributes mentioned earlier, coaches can create an organizational climate that allows all members to concentrate on the important task of athlete success in competition and not worry about the tiny details that only become noticeable when overlooked.

Uncommitted Community When coaches fail to build and nurture a community beyond the coaching staff and athletes, the organization finds little support from important resources.

Failed teams seldom attract a committed fan base who take pride in not just in the victories, but the values and traditions of the organization. Without their attendance and support at competitions, many programs fail to maintain a vital financial base. Institutional financial support can only carry a program so far. image of sport photo

Opportunities to fundraise and obtain donations from fans, alumni and boosters must be taken advantage of should a program wishes to achieve longevity and stability. Beyond just the financial, an uncommitted community provides no social or emotional support for the efforts of the coaches or the athletes. No one wants to play in an empty stadium.

While it may take a village to raise a child, it takes a community to build and sustain a successful program. Sports teams are integrated into our society like extended families. Successful coaches and high performing athletes are celebrated in the community. When they are deviant, the repercussions are severe.

More now than ever it is necessary for coaches to engage themselves and their program within their community positively.

Successful programs also have coaches who understand how to recruit prospects effectively. It’s not just about obtaining athletes with the best skills and athletic ability; coaches must screen for recruits who are coachable. Furthermore, it is paramount to identify recruits who

exemplify traits resembling the team culture. Obtaining just one ‘rotten apple’ can have a significant effect on the stability of a team’s culture and continued success. Traits found in prospects who make good recruits are those who are dependable, show a strong willingness to learn, and possess ample amounts of athletic ability, and are committed to the community.

Visionary Vagueness If you can’t see where you are going, it makes it tough to get anywhere. Athletic programs lacking a unifying and directing vision fail in providing the appropriate standards and expectations guiding individual and communal actions of the organization. Without a clear and workable vision, the organization becomes a rudderless ship at sea: with no intended direction to guide their efforts they simply float along at the whim of currents and tide—often with predictable and disastrous results.

A vision begins with understanding a program’s history. It is essential for coaches to research not only what went AUGUST 2017 techniques

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HOW TEAMS FAIL

well in the program’s past, but also identify what went wrong. The goal is to mold the organization in ways that will lead to success. Failed visions often stem from the absence of a coaches’ understanding of problems previously stunting growth and inhibiting the success of the program. As the old saying goes, ‘those who forget the past are doomed to repeat it.’

When a coach finally grasps these factors, they can begin building a vision based on the program’s successful traditions and change the factors that caused failure. To build on a program’s traditions, a coach must mine their philosophy—their personal and professional beliefs regarding what the program can and should be. In doing so, they develop a concrete vision containing appropriate programmatic goals and high standards of personal and athletic performance.

For this to be authentic, it must represent the personal attributes and commitments of the coaches themselves.

Successful program visions emanate from coaches who, themselves, possess 30

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high levels of passion and motivation.

Coaches deficient in these traits will have little success in instilling them in others. Athletes and team personnel will not see any value in making sacrifices or dedicating themselves to a program led by passionless leaders or coaches who provide little direction or support for the team’s success. They also will not embrace programs that lack direction, strategies for obtaining success, and standards for the actions required to realize that vision.

In creating a successful vision, a coach must answer these questions with clarity and conviction: a) what are the benchmarks that will define the success of my program, b) what is it I want from my personnel (i.e., athletes, assistant coaches, and support staff) to gain from being a member of the organization, and c) what do I desire supporters and outsiders to see in our program? By answering these questions, a coach can then craft a team culture built on a core set of values and practices, determine the investments necessary for athlete holis-

tic growth, and cultivate an environment where the individual contributions of all members produce a powerful collective action.

Summary Reasons for consistent program failure are plentiful, but failed programs seem to have several common characteristics.

Programs propelled into destruction are led by coaches whose domineering personalities and totalitarian leadership styles measure success in hardware and not humans. Contributing to their demise is a lack of organization, community commitment and most damaging--no vision. One of these characteristics alone can damage a program, but taken in combination they are lethal.

Consequentially it is critical for the coach to continually monitor and assess these factors to ensure they do not become prevalent in the program.

Building a successful program begins by recognizing the core values that will lead to success. As a focal point of the program, these values must be in harimage of sport photo

mony with the coachâ&#x20AC;&#x2122;s personality and leadership style. The same principles and values subsequently guide the actions and decisions of athletes, coaches, and staff as they embrace the programâ&#x20AC;&#x2122;s vision. These principles and values must also resonate with the personal commitments of all program members.

From these core values, an organizational structure is created to support the actions and aspirations of the program members as they seek success. Encouragement and respect for all are pervasive. Prosperous programs are thus comprised of cultures where individuals are inspired to achieve, as both athletes and people. This requires strong organizational skills on the part of the coach to provide experiences that promote healthy, holistic athlete development. Additionally, coaches must ensure they foster positive relationships with the outside community. This includes the immediate family members of the program, the campus community and those outside the institution. By creating and maintaining a reputation as a program that is rooted and connected to their community, coaches will all but ensure long-term support. Lastly, connecting the dots; visualizing ways in which these principles can and will support each other will place the program on the path to success.

References Vallee, C. N. & Bloom, G. A. (2005). Building a Successful University Program: Key and Common Elements of Expert Coaches. Journal of Applied Sport Psychology, 17: 179-196.

Marques R. Dexter is a doctoral student in the Department of Kinesiology at the University of Georgia and member of the Sport Instruction Research Laboratory. Prior to this, Dexter was an Assistant Track & Field coach, instructor and academic advisor at SUNY Cortland.

Nicole McCluney is a doctoral candidate in the Department of Kinesiology at the University of Georgia and member of the Sport Instruction Research Laboratory. Additionally, Nicole serves as the Managing Editor of the Professional Association of Athlete Development Specialists (PAADS) Research Digest.

Ashley Fallaize is a doctoral candidate in the Department of Kinesiology at the University of Georgia and member of the Sport Instruction Research Laboratory. Currently, Ashley is the Training & Education Manager at BlazeSports, overseeing their Institute for Adaptive Sports and Recreation.

Dr. Paul Schempp is a Professor in the Department of Kinesiology at the University of Georgia and the Director of the Sport Instruction Research Laboratory.

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kirby lee photo University of Georgia Athletics photo

Heavy Hammer

A study in the correlation between hammer throw and weight throw performance and development for U.S. collegiate throwers Donald G. Babbitt and Luke E. Johnson

T

he hammer throw and weight throw are track & field events that are closely related to one another in the context of both implement design and in how they are thrown.

The hammer throw is part of the outdoor program of track and field events which weighs 7.26kg and is 121.5cm in length for men and 4kg and 119.5cm for women. The indoor version of the hammer throw event is the weight throw, which is a much shorter implement at 41cm in length for both genders, while weighing 15.87kg for men and 9.07kg for women. Within the NCAA (National Collegiate Athletic Association), which serves as the de facto development system for track and field athletes in the United States between ages 18-23, both events are contested within a team scoring structure at the both the conference level and NCAA Championships.

This system can provide a challenge to the throwing coach, who is attempting to maximize the performance of their weight throwers during the indoor season, while also maximizing the performance of their hammer throwers in the outdoor season (Judge and Bingisser, 2006).

In breaking down and analyzing the relationship between hammer throw and weight throw performance, it is hoped it may provide the coach and athlete with a guide for what to expect as they improve their performance and results in both events. This in turn, may allow the coach to better format training and competition strategies for their throwers who compete in both the indoor and outdoor seasons.

In particular reference to the collegiate coach, this study will also provide an idea of performance expectations over the athlete’s collegiate career which may aid them in athlete selection when recruiting a potential thrower to their university.

It is well documented that short and heavy hammers have their place in training for developing hammer performance (Babbitt, 1995; Barclay, 1998; Bartoneitz et al, 1988; Bondarchuk et al, 1976; Wrublevsky, 2005; and Staerck, 1994).

However, it has long been a concern that the weight throw may be detrimental to potential hammer throwers development since there are technical nuances that are particular to weight throw success that may not directly carry over to hammer throw success (Losch, 1990). This may be especially true when throwing a weight for maximum distance in competition as opposed to training to aid one’s hammer technique, which could result in the hammer thrower inadvertently altering their technical model into “heavy hammer technique”. For this reason, it has been observed that there have been highly successful weight throwers of both genders, who do not go on to be as successful in the hammer throw. While these results may be driven mostly by choice, due to focusing on one event more than the other, this analysis will serve to provide an actual picture of how hammer/weight throwers at the NCAA level actually perform in these two events as they develop through their collegiate career.

Over the years, an axiom has been

cited, which compares potential hammer performance with that of weight performance suggesting that the hammer thrower’s performance in meters will be equivalent to their expected weight performance in feet (Nielsen, 2000). This theoretical formula, which has its roots based mainly upon observation, has been the primary reference utilized by collegiate coaches and athletes alike to project how hammer and weight performance may be correlated. However, Nielsen did calculate that the rotational velocities of the throwers for these performances were strikingly similar which made the rational for this formula seem plausible. One of the primary objectives of this analysis is to quantify how accurate this formula is by studying actual data from a large number subjects to observe the validity of this correlation for both male and female throwers. Potentially new trend lines will be proposed based on the data analysis in an attempt to uncover any differences between the theoretical trend line, in which r would be equal to 1, and the trend lines observed for developing male and female hammer/weight throwers generated by actual performance data.

Methods Three hundred and two of the top male and female hammer/weight throwers (151 men and 151 women) from the NCAA Division I level, who were active between 2010- 2016 were selected for performance analysis. It was necessary to use only throwers that had completed their colAUGUST 2017 techniques

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Heavy Hammer Table 1

Figure 1

Table 2

legiate eligibility so they had all four years to develop their hammer and weight throwing skills given that some subjects were not exposed to either or both events prior to entering collegiate competition. The personal bests for all throwers were recorded for both the hammer throw and weight throw at the end of their collegiate eligibility from the TFFRS list compiled

by USTFCCCA. This data was then graphed to develop a trend line plotting hammer performances expressed in meters against weight performance expressed in feet. Correlation coefficients were determined for both male and female performances with a set at <.001.

Further analyses were conducted to measure correlation coefficients for the same data for both genders by breaking down into five smaller subgroups broken down into hammer throw performance levels between 57.50-59.99m, 60.00-62.49m, 62.50-64.99m, 65.00-67.49m, and 67.50m+. A final analysis was performed, for both genders, by generating trend lines by taking results of the top 15 hammer throwerâ&#x20AC;&#x2122;s marks for weight and hammer in each of their years of collegiate competition. This was done in order to characterize their developmental progression for both events. A similar analysis was done for the top 15 weight throwers for both genders, as well, in order to compare with the results of the top 15 hammer throwers so as to expose similar or contrasting developmental patterns between the two groups. Alpha was set at <.001 for all analyses.

Results

Figure 2

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Hammer throw and weight throw data for the 151 male hammer/weight throwers was entered on a scatterplot graph and produced a significant linear trend line revealing a strong correlation between hammer and weight throw performance (r=.7521, p<.001).

Linear regression analysis produced a slope for the trend line that was calculated to be (weight throw distance in feet)= 1.0003(hammer throw distance in meters) + 1.4339 (see Figure 1).

Correlation coefficients generated for subgroups within the larger male group saw significant correlations between hammer and weight throw performance for the 67.50m+ group (r=.7931, p<.001) and the 57.50-59.99m group (.4658, p<.05).

Comparisons within all other subgroups did not yield and significant findings (see Table 1).

Hammer throw and weight throw data for 151 female hammer/weight throwers also produced a significant linear trend line revealing a strong correlation between hammer and weight throw performance (r=.5219, p<.001), although not as high as for the male throwers. The slope of the trend line that was generated by linear regres-

Heavy Hammer Figure 3

Table 3

Figure 3

Figure 4

sion analysis was calculated to be (personal best of weight throw in feet)= 0.7903(personal best of hammer throw in meters) + 16.189 (see Figure 2). There were no significant correlations found within the subgroups of the larger female group (see Table 2).

In an effort to more deeply examine any differences between high performing hammer throwers and high performing weight throwers, performance data, which spanned the full collegiate careers of the top 15 male hammer throwers from the 151 thrower group, was plotted. This analysis revealed a significant correlation between hammer and weight performance for this group (r=.7362, p<.001). The slope of the regression line for the top 15 male hammer throwers was calculated to be (distance of weight throw in feet)= 0.8141(distance of hammer throw in meters) + 14.558 (see Figure 3).

Findings of similar significance were obtained for the group consisting of the top 15 male weight throwers (r=.7922, p<.001) (see Table 3). The slope of the regression line for the top 15 male weight throwers was calculated to be (distance of weight throw in feet)= 0.7966(distance of hammer throw in meters) + 17.499 (see Figure 4).

In addition, performance data for both the hammer and weight throwing events, extending over the full collegiate careers of the top 15 female hammer throwers was plotted, which revealed a significant correlation between hammer and weight performance for this group (r=.5617, p<.001)(see Table 4). The slope of the regression line for the top 15 female hammer throwers was calculated to be (distance of weight throw in feet)= 0.6024(distance of hammer throw in meters) + 27.672 (see Figure 5).

Analysis of the top 15 female weight throwers also produced a statistically significant result, that was very close to that seen for the top 15 female hammer throwers (r=.5569, p<.001) (see Table 4). The slope of the regression line for the top 15 female weight throwers was calculated to be (distance of weight throw in feet)= 0.4883(distance of hammer throw in meters) + 39.663 (see Figure 6).

Discussion It was not surprising to see that there was a correlation between hammer throw and weight throw performance for both men (r=. 7521) and women (r=.5219). The events are very similar and share closely related technical patterns and training methods, so it would make sense that general improvement in one event could lead to improvement in the other.

Bondarchuk (2007) had previously reported similar correlations for male hammer/weight throwers of .452 for hammer throwers between 60-65m, .586 for hammer throwers between 65-70m, and .677 for hammer throwers between 70-75m. Even though both genders had statistical significance at p<.001,

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Heavy Hammer Table 4

Figure 5

Figure 6

Figure 7

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the women did have a lower correlation coefficient than the men. This difference is illustrated when examining the slope of the trend lines for the performance data of the both genders in Figures 7.

Figure 7 shows how the men’s trend line follows a slope that closely resembles the circumstantial formula of weight performance in feet equaling the hammer performance distance in meters that had been put forth by Nielsen (2000). The slope only differs in that the corresponding weight distance is consistently one and a half feet more than the meter distance for the corresponding hammer performance throughout the entire range of hammer performance. In contrast, the slope of the women’s trend line follows a slightly different path in which the weight performance is much greater than the corresponding hammer performance in meters for corresponding hammer performances below 65m. The women’s weight throw performance gap then steadily narrows as the hammer performance increases to eventually intersect with the theoretical model at 75 meters.

Given these results, it appears the men’s hammer to weight relationship appears more stable throughout the range of performance, while the women’s model shifts it’s relationship to be more in line with the theoretical correlation trend line as the hammer performance level rises. Why this difference in performance trend lines is observed may be due to differences in the implement weight to body strength ratio between male and female throwers. It is postulated that greater initial success in female weight throw performance may be easier to achieve when compared to similar male weight throw performance due to the female group’s greater size and strength relative to the actual implement. Previous studies have indicated that maximum strength may play a significant role in improving performance for the hammer and/or the weight throw (Judge et al 2010; Stone et al 2003). This notion may also be supported by Judge et al’s (2011) finding that height had a significant correlation to personal best in the weight throw for females, while not being statistically significant for male weight throwers. As Figure 7 indicates, while the performance levels continue to improve, the disparity between female’s weight performance and hammer performance continues to diminish. This could suggest that as throwing performance increases, that technical execution begins to overtake superior anthropomorphic measurements and absolute strength as the primary driver of successful performance.

To investigate this further, an examination

AUGUST 2017 techniques

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Heavy Hammer Figure 8

Figure 9

of the performance data for both genders, by creating subgroups of 2.5 meter performance intervals, was used to unearth more differences between the two groups. Statistical significance was found only for the top male subgroup (67.50m+), and weakest male subgroup (57.50-59.99m), while none of the female groups showed any statistical significance. It appears from the data that only the highly skilled male hammer/weight throwers and the lowest performing male hammer/ weight throwers produced a statistical correlation.

It is theorized that both groups, while of differing performance levels, may have exhibited a relatively consist pattern of technique that limited the variety of ways they could express their technique to produce distance, either in terms of top performances (67.50m+ group), or in relatively low performances (57.50-59.99m group).

In all other performance groups for 44

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both genders, the lack of statistical significance may have been due to the possibility that performances in these ranges were the result of a much broader range of techniques and approaches, thus causing a wide variety results. This could mean that throwers in these groups had a combination of technical and physical ability that ranged from being much more suited for better performance in the weight than hammer to throwers whose abilities were more suited for superior hammer performances.

In other words, these results suggest there are many ways to progress oneâ&#x20AC;&#x2122;s performance in hammer/weight throwing for females through all levels of performance, while this may only be true for males who throw between 60.00m-67.50m.

A deeper investigation into the various performance correlations sought to identify any differences in the way both male and female throwers prog-

ress based on a concentration toward a specific implement. It was thought that throwers who trained primarily for success in the weight during the indoor season, and also threw the hammer during the outdoor season would show a different trend line for performance compared to throwers who focused primarily on hammer throw development and only threw weight as a secondary emphasis during the indoor season.

In breaking down the data further by analyzing the results from only the top 15 hammer and weight throwers of each gender, it was believed that these groups would provide representative progressions for either highly motivated weight throw specialists or hammer specialists.

Examination of Figure 8 shows that both male and female top hammer groups were substantially above the theoretical trend line for performances early on in their collegiate careers. Both groups trended toward the theoretical trend line as they progressed in performance, with the top female hammer throwing group eventually intersecting with the theoretical trend line when they reached hammer performances of 70 meters, while the top male hammer thrower group did not intersect with the theoretical trend line until performances of 75 meters were reached. The top weight groups of both genders showed slightly different developmental relationships than that of the top hammer throwing groups. The slopes for both the top male and female weight throwers, which are depicted in Figure 9, are quite similar in slope for both gender groups seen in Figure 7. However, one difference that was observed was the differential between the male and female weight performances and the theoretical trend line was more pronounced at the 55 meter hammer performance level (see Figure 9). The top weight thrower trend lines both gravitate to the theoretical correlation trend line as performances improve and go on to resemble the trend lines seen in Figure 7, once they reach the 70 meter mark for hammer performance.

Final comparisons were made between the top hammer throwers and top weight throwers of each gender (see Figures 10 & 11). For both genders, it was observed that the trend lines for each event group were close to parallel in slope, however, there was a greater gap between the slope for the top weight groups and the theoretical trend line when compared with the slopes for the top hammers throwers. It may be possible that this gap is explained by the more intricate nature of the hammer throw technique which has an acceleration path of nearly 11 meters per turn (Isele and Nixdorf, 2010) when compared with only 8.5 meter per turn for the weight throw. The longer ball path used to generate final release velocity will require more timing for superior results and less reliance on strength than needed for the weight (Judge and

Heavy Hammer Figure 10

Figure 11

Bingisser, 2006), which could explain why the weight throwing group has better weight performances relative to their hammer performance until they can throw close to 70 meters with the hammer. This finding reinforced the idea that the theoretical trend line may apply more closely to throwers whose primary focus is on training for the hammer, and for whom the weight throw is a secondary event.

Conclusion The results from this study seem to suggest that the actual trend line generated by real performances showing the correlation between collegiate male hammer throw and weight performance more closely resembles the theoretical trend line suggesting the hammer performance in meters will be equivalent to weight performance in feet, when compared with that for collegiate female hammer/weight throwers. Within these groups it appears that the theoretical trend line 46

techniques AUGUST 2017

may be more applicable to throwers who focus more on hammer throw than weight throw, rather than throwers who focus on weight throw as their primary event. While this study serves to uncover some initial findings about the relationship between hammer and weight performance, further study will be necessary to look deeper into the causes for the differences between genders and event performance.

Factors such as height, weight, strength levels, training age, and choice of technique can be examined more closely to give both the coach and athlete a better idea of what to expect from their training and preparation in these events so they can organize and adjust training to optimize future results.

training in the womenâ&#x20AC;&#x2122;s hammer throw. Modern Coach & Athlete, 36:3, 15-17.

Bartoneitz, K., Hinz, L., Lorenz, G., and Lunau, G. (1988), The view of the DVfL of the GDR on talent selection, technique and training of throwers from beginner to top level athlete. New Studies In Athletics, 1, 39-56.

Bondarchuk, A. (2007), Transfer of Training in Sport, Michigan, Ultimate Athlete Concepts.

Bondarchuk, A., Ivanova, L., and Vinnitchuk, W. (1976), Training with light and heavy implements. Modern Coach and Athlete, 14:2, 21292130.

Isele, R, Nixdorf, E. (2010), Biomechanical analysis of the hammer throw at the 2009 IAAF world championships in athletics. New Studies in Athletics, 25:3/4, 37-60.

Judge, L., and Bingisser, M. (2006), Rethinking your approach to training for the weight throw, Track Coach, 176, 1-7.

Judge, L., Bellar, D., McAtee, G., and Judge, M. (2010), Predictors of personal best performance in the hammer throw for U.S. collegiate throwers, International Journal of Performance Analysis in Sport, 10:1, 54-65.

Judge, L., Bellar, D., Turk, M., Judge, M., Gilreath, E., and Smith, J. (2011), Relationship of squat one repetition maximum to weight throw performance among elite and collegiate athletes, International Journal of Performance Analysis in Sport, 11:2, 209-219.

Losch, M. (1990), Training derivations for biomechanical studies in the hammer throw. IAAF Technique in Athletics Conference Proceedings, Volume II. Nielsen, D. (2000), Spinning hammer and weight throw numbers, Track and Field Coaches Review, 73:2, 27-30.

Staerck, A. (1994), Suggestions on different mass hammers for women. Athletics Coach, Summer, 26.

Stone, M., Sanborn, K., Oâ&#x20AC;&#x2122;Bryant, H., Hartman, M., Stone M., Proulx, C., Ward, B., and Hruby, J. (2003), Maximum strength-power-performance relationships in collegiate throwers, Journal of Strength and Conditioning Research, 17:4, 739745.

Wrublevsky, E. (2005), Management of the training process in qualified female hammer throwers. New Studies in Athletics, 20:4, 25-31.

References

Don Babbitt is the Associate Head Coach at the University of Georgia. Don has coached the throwing events at UGA for nearly 20 years and is a regular contributor to techniques.

Babbitt, D. (1995), Training with hammer of various weights. USA Thrower, 2:4, 16-17.

Barclay, L. (1998), Basic concepts for

Luke Johnson is a volunteer throws coach at UGA. He was an All-American shot putter at the University of Minnesota where he earned a degree in Kinesiology.

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image of sport photoS

Skill Acquisition Evidence-based Practices for Detecting and Correcting Errors Matthew T. Buns, Ph.D. and Tyler Naumowicz

F

eedback is one of the most important factors in skill acquisition and one of the most difficult for coaches to master. Feedback is information about performance and any instructional situation will be significantly less effective if feedback is not provided correctly (Bilodeau, 1966).

Thus, providing appropriate feedback is one of the most important responsibilities of a coach. Coaches often spend so much time setting goals and designing practice sessions, they do not effectively plan for or provide the most effective feedback.

The purpose of this article is to detail the feedback factors that optimize learning and performance in track and field. Best practices for the role of feedback in motor performance include 1) precision, 2) timing, 3) mode, and 4) frequency with which feedback is provided. Each is equally important and together they form the guidelines for providing effective feedback to learners of motor skills.

The Precision of Feedback Knowing the appropriate level of precision of feedback is an important consideration for track and field coaches.

Precision of feedback refers to the type of information, from general (“Push harder”) to specific (“Your elbow was bent two degrees too much”). The precision of feedback typically stems from two broad categories referred to as qualitative and quantitative feedback. Qualitative feedback statements contain information about the direction of an error, without referring to the magnitude of the error.

Examples of qualitative feedback state-

ments include: “You lifted your knee too far.” “Bend your elbow less.” “That was faster than last time.” Note that in each case the feedback statement indicates a needed direction of change but not how much of a change should be made.

Adding information to a feedback statement about how much change is needed increases the precision of feedback. The previous examples demonstrate increased precision by adding modifiers such as: “You lifted your knee a little too far.”

“Bend your elbow just a little bit less.”

“That was a lot faster that time.” Although these qualitative feedback statements contain useful information, they still lack the informational properties most useful for initiating corrections.

The precision of feedback is further increased by providing information not only concerning the direction of an error, but also about the magnitude of the error. Quantitative feedback provides information about both the direction of an error and its magnitude in numerical terms .Examples of quantitative feedback statements include these: “That was six inches too far.” “Bend your elbow to a 45-degree angle.” “Your time was 22.4 seconds.” “Your flexion has improved by five degrees.” Because these statements contain more precise information about needed corrections than do qualitative statements, both the coach and athlete are more likely to interpret it in the same way.

How precise should coaches be? The research on feedback precision suggests that extrinsic feedback does not need to be extremely precise to be effective. In making decisions regarding the precision

of the feedback to provide to athletes, coaches would be well advised to consider the complexity of the task relative to the usefulness of information provided by feedback. For beginners, less precise information is better because they are just trying to get a general idea of the correct relative timing pattern. At that point, all athletes need to know is general information about the relative amount and direction of their errors. You might tell a beginning high jumper that her takeoff was a bit too early. However, once she achieves a higher level of technical skill, the athlete would benefit from more precise feedback that helps her fine-tune her movements (e.g., the duration in milliseconds of her final two steps).

As a general guideline, feedback should be provided with as much precision as a learner can meaningful interpret, and this typically implies using quantitative feedback as preferred to qualitative feedback. There is a point, however, where too much quantitative information can be provided in a feedback statement. Figure 1 illustrates the relationship between feedback precision and learning. Learning is enhanced as the precision of feedback becomes greater, but only up to a point.

After that point, continued increases in precision become less useful to learners, who are unable to effectively respond to the more precise corrective information, which leads to diminished performance and learning.

One way to promote technical skill development while increasing feedback precision is through bandwidth feedback. To do this, establish a performance AUGUST 2017 techniques

49

skill acquisition Figure 2. A comparison of errors made with time to think about feedback (strategy) versus without feedback (no strategy).

Source: Thomas, K. T., Lee, A. M., & Thomas, J. R. (2008). Physical education methods for elementary teachers.

Human Kinetics.

Figure 1. Precision of Augmented Feedback. Source: Adapted from Edwards, W. H. (2010). Motor learning and control: from theory to practice. Cengage Learning.

bandwidth—the amount of error you will tolerate before providing extrinsic feedback. As long as an athlete’s performance remains within a tolerable zone, there’s no need to give feedback.

Normally coaches will want to allow a wider bandwidth and provide more general feedback for athletes who are learning a new skill than for performers whose skill level is more advanced. The bandwidth for a beginning pole vaulter, for example, would allow any movements that conform to the basic relative timing pattern. If they don’t, a general feedback statement might be provided. As the athlete’s skill level improves, the bandwidth would be narrowed so that feedback is given to correct even small performance deviations. If her stride length was a bit too short, you might tell her to increase it by an inch or two. Since bandwidth feedback allows you to give feedback less often, athletes derive the same benefits as they do any time you reduce feedback frequency. It is the coach’s responsibility to determine a performance bandwidth for athletes that allows them to improve their technical skills as much as possible without outside assistance.

The Timing of Feedback Another aspect of feedback that coaches must consider involves the timing of feedback delivery. How soon after an attempt should feedback be given? Once athletes are provided with feedback,

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how soon should they make their next attempt? There is no shortage of research that has been directed toward answering these questions.

Athletes need sufficient time to be able to process the information provided through feedback and plan how they are going to perform their next attempt. The time period between the presentation of the verbal feedback and the athletes’ subsequent performance attempt is called the postfeedback interval. Once athletes have feedback, they may need 10 or more seconds to make a plan to correct the movement before their next attempt. You can see from Figure 2 how much error is reduced when athletes who would not ordinarily use a strategy are given one to solve a specific task. Children have greater processing limitations than adults. Unfortunately, on their own, children take almost no time to consider the feedback. So coaches need to allow the time to encourage athletes to use the time to make corrections, particularly when working with young athletes.

The more complex the technical skill, the more time athletes will need to evaluate their performance. By challenging athletes to evaluate their own errors and come up with possible solutions before giving them feedback, coaches facilitate both skill development and the athlete’s capability of detecting and correcting their own mistakes. This means coaches may need to tolerate silence for a longer

period of time when asking athletes to evaluate something relatively complex.

To facilitate this process, coaches can ask athletes how they are going to execute the next attempt, or more specifically what they are going to do differently in comparison to the previous attempt.

Providing more time after the feedback (the postfeedback interval) can improve performance.

A common misconception about feedback is that it must be immediate. Many coaches assume that feedback is most effective immediately following performance. The reasoning behind this notion is that athletes are subject to a limited attention span. Therefore, coaches conclude that feedback should be provided immediately because the athlete will begin to forget elements of their practice attempts as soon as each is completed.

Although one might expect immediate feedback to be most beneficial because it allows athletes to link the performance with the outcome or quality of the movement before their memory of the movement production fades, immediate feedback actually prevents athletes from reflecting on the movement and promotes passive learning. Table 1 outlines three common misconceptions associated with the provision of feedback and provides best practices for each misconception (Haibach, Reid, & Collier, 2011).

Although feedback must occur before the next trial of a task, it does not have

skill acquisition

Table 1. Misconceptions About and Best Practices for Extrinsic feedback. Source: Haibach, P., Reid, G., & Collier, D. (2011). Motor learning and development. Human Kinetics.

to occur immediately. Adams (1987) examined feedback given immediately, one day later, and one week after the performance reported no differences in learning. Immediate feedback is not important to learning motor skills. In fact, the athlete may become so reliant on the immediate feedback they will not develop the critical problem-solving skills necessary for performing the skill without the guidance of the coach. So if coaches cannot provide feedback at the end of practice or competition on a given day, they can do it before practice begins the next session. This effect is so strong that feedback given after several trials is just as good as immediate feedback provided one trial at a time.

The Mode of Feedback

Figure 3. Example of how a series of photographs can provide kinematic feedback.

Biofeedback. Biofeedback is a form of feedback that provides concurrent information related to the activity of physiological processes. The information is used to shape behavior during performance. For example, one of the most commonly used forms of biofeedback in track and field is the heart rate monitor. Heart rate monitors have been used to help distance runners adjust their intensity levels through a workout based on heart rate data.

Figure 4. Retention and Relative Frequency of Feedback. Source: Adapted from Edwards, W. H. (2010). Motor learning and control: from theory to practice. Cengage Learning.

The modality usually indicates whether the feedback is verbal, visual, or physical. Although we often think of feedback as verbal, most coaches have used many forms of nonverbal feedback.

Video Feedback. Athletes may think they are performing a particular skill correctly until they see for themselves via pictures or a video. Although video feedback can be quite useful, coaches must consider several factors when using videos, such as the time period during which the video is presented, the skill level of the athlete, and whether the video is supplemented with additional feedback from the coach. Research studies have shown that video feedback used for less than five weeks resulted in no improved performance (Rose & Christina, 2006).

Providing video feedback without cueing the learner to specific aspects of the movement is also ineffective, especially for the novice athlete, and may actually be detrimental to performance. This is likely because the video feedback provides “too much information” for learners who do not understand what they should be looking for in the video or how to interpret this information (Coker, 2009). When athletes are introduced to video feedback, they are often more focused on their visual appearance (e.g., “How attractive do I look?”) than on their movement patterns or performance.

Because of this preoccupation with kirby lee photo

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irrelevant factors, further instruction is ineffective until learners have adjusted to viewing themselves on video. Advanced athletes, on the other hand, know what key aspects of the movement pattern they need to focus on and can benefit from viewing videotapes of their performance with little to no supplementation.

Visual Feedback. Visual feedback, which can be very beneficial during the learning process, can take the form of visual displays of kinematic feedback. The most commonly used forms of kinematic feedback are pictures, illustrations, and video replays of limb position, velocity, or acceleration (see Figure 3). For example, illustrating the changing form and correct alignment of the body throughout a pole vault attempt provides information on the movement and is supplemental to intrinsic sources of feedback. It is easy to separate the movement patterns in field events from the goal outcome, so learners of skills like these are likely to benefit from kinematic feedback.

The Frequency of Feedback One final guideline has been developed concerning the effective delivery of feedback, and that entails how frequently feedback should be provided to be of the most benefit. Traditionally, researchers and coaches assumed that the more frequently feedback was provided following performance attempts, the greater the gains in learning. This conclusion dates back over 100 years and is based in part on Thorndike’s law of effect (1905) and the principles of operant conditioning.

If feedback were essential in improving performance, it was argued, then obviously the more frequently feedback was delivered, the more reinforced the correct movement would become. One reason that this conclusion continues to exert an influence on many coaches thinking today is that frequent feedback does promote a practice performance. However, research conducted in the last 50 or 60 years has yielded results contradictory to the accepted wisdom that the greater frequency of feedback, the greater the degree of learning that would occur.

Bilodeau and Bilodeau (1969) investigated the effects of manipulating feedback frequency on learning. In a series of studies, they were the first to observe that reducing the relative frequency of feedback enhanced learning outcomes when compared to higher relative frequencies of feedback. That is, in their studies Bilodeau and Bilodeau found that high

amounts of feedback actually depressed optimal learning, whereas reducing relative frequencies of feedback improved learning outcomes. At some point, however, reducing the frequency of feedback further begins to impede further learning and, in fact, actually begins to depress learning. A generalized curve summarizing these findings is shown in Figure 4.

Of note, this effect was observed only for retention measures (i.e., learning), and not for physical performance (which appeared to benefit as the frequency of feedback increased).

Generally speaking, current research suggests that too much feedback is detrimental to learning (Salmoni, Schmidt, & Walter, 1984). Early in learning, more feedback is better; in fact, feedback on every attempt is great. When the athletes have practiced more, coaches can provide feedback less often, perhaps half of the time. Late in practice (or towards the end of a season) only give feedback once in a while. If you averaged the amount of feedback across learning, from early to late, the average would be that 50 percent of the attempts received feedback. The reason is that athletes can become dependent on extrinsic feedback, which means that they do not learn to detect and correct errors themselves. For example, a javelin thrower receives feedback after each of 10 attempts. If the feedback is “Keep the javelin lower,” what do you think the athlete will try to do on his next throw?

Keep the javelin lower. If he hears “Follow through better,” he will attempt to follow through better on the next attempt.

In other words, each time the athlete receives extrinsic feedback, he immediately tries to incorporate the feedback on the next attempt. The problem is that by making these moment-to-moment corrections, the athlete is unable to achieve much stability in his performance. As a result, he doesn’t learn as much about the relationship between what he is doing and the result that he is getting. Although performance may appear to be improving with more frequent extrinsic feedback, the athlete is probably not learning why this is happening. When he ceases to get receive feedback, the athlete’s performance is likely to regress to its previous level. Thus, excessive feedback promotes passive learning, so that the athlete does not develop the critical problem-solving skills necessary for performing the skill without the guidance of the coach. To be independent performers, athletes must learn to detect and correct their own

errors.

One way to increase the amount of feedback coaches provide without overloading athletes with too much information is to use summary feedback or average feedback after a practice session. Summary feedback informs athletes how they performed on each of several practice attempts, while average feedback highlights general tendencies in their performance (Edwards, 2010). For a long jumper’s last five jumps, summary feedback might be that her plant foot landed beyond the takeoff board on her first, third, fourth, and fifth jumps and on the takeoff board during her second jump.

Average feedback for the athlete might be that her plant foot landed slightly beyond the takeoff board for the five attempts. An important issue to consider when giving summary feedback or average feedback is the number of performance attempts to include in the feedback statement.

Generally speaking, the more complex the technical skill or the less experienced the athlete, the fewer attempts you should include in the feedback.

Practical Considerations for Detecting and Correcting Errors In light of the shortcomings associated with too much feedback, coaches are tasked with the responsibility of deciding how often to provide to facilitate rather than impair an athletes’ skill development. One way to do this is to reduce the frequency of extrinsic feedback whenever it becomes apparent an athlete is becoming more proficient. To provide helpful feedback, coaches must be able to both detect errors in athletes’ performance (e.g., see a high jumper turning his back before jumping over) and offer possible solutions for the problem (e.g., ensure run-up curve is not too tight or lean is slightly into the curve). The process of helping athlete’s correct errors begins with observing and evaluating performance.

Coaches must develop a critical eye for proper mechanics, correct errors, and praise progress. One of the most common coaching mistakes is providing inaccurate feedback and advice on how to correct errors. As a rule, coaches should see the error repeated more than just occasionally before attempting to correct it (American Sport Education Program, 2008). Although there is no substitute for a coaches own mastery of the skill, Koch (2017) offers practical considerations detecting and correcting errors in track AUGUST 2017 techniques

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skill acquisition

Skill

Error

Sprints Blocks Front knee is bent less than 90

degrees or more than 90 degrees

Less than 90 degrees move starting blocks back because it is set too close to starting line. More than 90 degrees - move blocks forward

Blocks Athletes shoulders are not

far enough forward

Move shoulders forward until they are slightly in front of hands - may need to move blocks forward

Blocks Drive out of blocks is not

explosive

Athlete’s forward leg needs to forcefully push against the block

Sprinting

Arms and shoulders twist and rotate

Keep torso parallel to the direction athlete is running

Sprinting

Athlete is too tense, shoulders are up high, holding breath, and face is strained

Practice running relaxed, focus on proper breathing

Hurdles Blocks to Hurdle 1 Athlete is too close to

hurdle after 8 steps

Try having athlete switch to 7 steps in order to create more room to hurdle

Running Between Strides are too long and

Hurdles slow

Shorten up the stride and lower heal recovery. Make the three steps between hurdles quicker

Trail Leg Trail knee or foot hits

hurdle

Trail leg need to cycle through and be parallel to top of hurdle, foot should stay flexed towards shin and not hang down

Hurdle, don’t jump. Step through the hurdle as if it wasn’t there. Keep head at same level the entire race and raise the hips instead

Over the Hurdle Athlete is too far in air

over hurdle

Relays 4 x 400 Handoffs Athlete doesn’t take three

steps and look back for

baton

Since the exchange isn’t as fast, looking back and locating the baton is the best. Take three strides and look back with arm raised high.

4 x 100/200 Athlete looks back for

Handoffs baton

4 x 100/200 relays require blind or semi-blind handoffs. Practice exchanges before relay and put tape on track to know when to start running in the exchange zone in order to avoid having to look back.

4 x 100/200/400

Handoffs

Focus on receiving baton in the middle of exchange zone for optimal handoff.

Athlete receiving baton steps outside of exchange zone

4 x 100/200/400 Incoming runner runs into

Handoffs outgoing runner

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Outgoing runner may be starting too late or not standing close to the correct side of the lane (outside edge of lane).

Table 2. Detecting and Correcting Common Errors Source: 2016 Olympian Coach Mark Koch of Concordia University, St. Paul and field. These include common performance errors in track and field and possible feedback statements that could be used for fixing the errors (Table 2).

It is not uncommon for athletes to demonstrate more than one error in performance on the same attempt. For example, consider an experienced sprinter having trouble with his block starts. He tends to position his hips too low in the “set’ position and often takes his first step with the wrong foot. What is a coach to do?

First decide which error to correct first – athletes learn more effectively when they attempt to correct one error at a time.

Determining whether one error is causing the other; if so, have the athlete correct that error first because it may eliminate the other error. In the previous example, raising the hips might help the athlete drive off the front pedal of the starting blocks. However, if neither error is causing the other, athletes should correct that error that is easier to fix and that will bring threats improvement when remedied. Improvements in the first area can motivate athletes to correct the next error.

As long as athletes are attending to relevant sources of intrinsic feedback, they don’t need (or usually don’t want) additional information from the coach. Therefore, a good rule offered by Edwards

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Skill

Error

Correction

Distance Posture Intentional forward lean

Run erect, with the torso directly above the hips, to increase knee lift and stride length

Arm-Action Swinging arms past the

mid-line of body

Run with a constant arm angle of approximately 90- degrees until the final stages of the race

Shoulders Tense or shrugging

shoulders

The arms should move forward and back with the shoulders back, hands moving forward from a point just behind the hips, slightl across the chest, up to a point near the shoulders

Hands Clenching of the fists

The hands should be cupped and relaxed

Foot Strike Heel-first foot strike

(over striding)

Middle of the foot should strike the ground with the weight toward the ball of the foot and toes dorsiflexed

Long Start/approach/drive Stands straight up, no Jump drive phase from the start

Lean forward like a phase sprinter and drive, using long and purposeful strides

Rhythm phase Fails to run with sufficient

knee lift and does not raise

the torso from the drive phase

Attack phase Athlete does not run close

enough to maximum peak

and turn over frequency

does not increase

Start to run more upright, run with higher knees. Run should feel more bouncy

Set up Athlete goes straight from

attack phase to take off

and does not set up the jump

Athlete needs a penultimate step (second Flat-foot ground strike with non-jump foot to set up take off and create lift

Plant Foot Athlete’s plant foot is too

far out in front of them

Make sure plant foot is underneath the body to prevent too much blocking

Long Jump Take-off Athlete stutter-steps and

takes off on the wrong foot

and looks down at board

Verify run-up and start point. Practice run the exact same way each time

Landing Athlete lands upright

Increase forward reach and momentum. Increase height so legs can be fully extended before entry into sand

High Arm Drive Athlete does not use arms Jump

Utilize arms to facilitate added force into take-off without losing top-speed momentum

Take-off Athlete takes off too close

to the bar and hits it

Ensure last strides are far enough away from bar so athlete can lean back enough before take-off

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This phase should be more of a sprint. As athlete approaches take off, turn over should increase, running tall.

(2011) to keep in mind when considering when to give feedback is “When in doubt, be quiet.” Recent research indicates that athletes benefit more from feedback when they ask for it than when someone else (e.g., the coach) decides they need it.

By establishing good communication with athletes, coaches should be able to discern when they need feedback and when they don’t.

When athletes enjoy open communication with the coach, they are more likely to request extrinsic feedback when they need it.

Conclusion As aforementioned, feedback is one of the most important factors in skill acquisition and one of the most difficult for coaches to master.

Feedback helps guide the performer toward executing the proper movement pattern, motivates the athletes, and reinforces successful performance. However, findings also suggest that coaches should resist the temptation to provide feedback more frequently and instead allow athletes to practice their skills on their own. A general rule is for the coach to provide feedback more frequently during initial learning and progressively less frequently as skill levels improve. Just as practice should be designed around the individual and the task, feedback should be individualized to the learner and the task. The more difficult the technical skill, the more likely it is for athletes to request a coaches feedback.

The most important principle to remember when it comes to deciding how much feedback to give is “Keep it simple.” Simple, however, does not mean simplistic.

On the contrary, feedback

Skill

Error

Correction

High Take-off Angle Body leans into bar.

Jump

Angle must be up. Keep Take-off is not upright torso leaned back and away from bar. Brake forward momentum with plant-foot heel strike to create upward take-off

Over the Bar Athlete’s backside knocks

the bar down

Hips need to be raised to clear the bar. Squeeze the glutes, throw head back to create arch over back

Over the Bar Athlete comes down on

the bar while in the air

This is caused by the athlete slowing down before take-off. Keep the speed up before planting the foot to ensure the athlete clears the bar and lands further back in the pit

Triple Approach Too fast, too early Jump

The coach places cones at the back of the runway and instructs the athlete to “drive” a specified distance in the early part of the approach.

Take-off Direction of take-off

is too vertical

Focus on “running” off the board, rather than creating vertical lift. Takeoff in triple jump should be the lowest trajectory of the four jumps

Hop The first phase is too long

Full-jump “models” where the athlete travels no farther than a specified distance (marked with cones) on each phase, especially the first phase

Step The second phase is too

short. It resembles the

penultimate step in the

long jump.

The athlete performs bounding drills that focus on the middle phase.

There needs to be a mindset change wherein the athlete becomes comfortable with the fact that the last phase may feel

Jump Over-rotation

The last phase of a triple jump, for almost all athletes, should resemble the “hang” long jump technique. A long, stretched out body with arms raised high over the head will prevent over-rotation prior to landing

Shot Put Glide The athlete’s base is too

wide

The (right-handed) athlete stands in the ring with her left foot at the toe board and her right foot at the back of the ring. Then, the athlete pulls the right foot into and beyond the middle of the ring

should provide athletes with the most helpful information possible. Keeping extrinsic feedback simple means giving athletes the type of feedback that is most relevant at a particular moment. In other words, quality is more important than quantity. The coach’s goal is to provide athletes with the type, amount, and frequency of extrinsic feedback that forces them to attend to the intrinsic feedback—the feedback they sense themselves—that is relevant for successful performance. The more athletes do this, the better they will be able to perform in competition without assistance. One of the major responsibilities of a coach is to provide feedback, but at the same time, help athletes learn to detect and correct their own errors. Ironically, good coaches may eventually coach themselves out of a job!

References Adams, J.A. (1987).

Historical review and appraisal of research on the learning, retention, and transfer of human motor skills. Psychological Bulletin, 101, 41-74 American Sport Education Program (2008). Coaching Youth Track and Field.

Champaign, IL: Human Kinetics.

Bennett-Yea, S., Bowler, V., Durden, W., Lenox, D., Murphy, R., Sirianni, K., & Zackodnik (2007). Athletics Coaching Guide: Planning an Athletics Training and Competition Season. Special Olympics.

Bilodeau, I.M. (1966).

Information feedback.

In E.A. Bilodeau (Ed.), Acquisition of skill (pp. 225296). New York: Academic AUGUST 2017 techniques

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skill acquisition

58

Skill

Error

Correction

Shot Put Glide The glide is to vertical

The athlete focuses on sliding across the ring.

The feet should feel like they’re sliding across ice rather than jumping and landing

Release The release angle is too

high

The angle of release should come from the angle of the torso, and not from the hand applying a vertical force via the deltoids. Focus on having chest toward the sky, and thinking of a flat bench press rather than a military press or even an incline bench press

Reverse The athlete watches the

shot land (and often falls

out of the ring)

The coach stands behind the ring and holds up a certain number of fingers.

The athlete is required to tell the coach after each rep how many fingers she saw

Discus Back of Ring

Keep the left-shoulder high. Drill spinning 360 degrees on the left foot with the left shoulder higher than the right shoulder

Upon initiation of the rotation, the (right-handed) athlete dips the left shoulder into the middle of the ring

Press.

Coker, C.A. (2009). Motor learning and control for practitioners. New York: McGrawHill.

Edwards, W. H. (2010). Motor learning and control: from theory to practice. Cengage Learning.

Haibach, P., Reid, G., & Collier, D. (2011). Motor learning and development. Human Kinetics.

Rose, D.J., & Christina, R.W. (2006). A multilevel approach to the study of motor control and learning (2nd ed.). San Francisco: Pearson Benjamin Cummings.

Thomas, K. T., Lee, A. M., & Thomas, J. R. (2008). Physical education methods for elementary teachers. Human Kinetics.

Thomas, J.R. (2000). C.H. McCloy Lecture: Children’s control, learning, and performance of motor skills.

Research Quarterly for Exercise and Sport, 71, 1-9.

Thomas, J.R. M.A. Solmon, M.A., & Mitchell, B. (1979).

Precision knowledge of results and motor performance; Relationship to age. Research Quarterly for Exercise and sport, 50, 687-698.

“Sprint” to Middle The athlete has shoulders

too far over the feet

In the move toward the middle of the ring, the athlete needs to keep the chest back. This can be drilled by thinking of the feet “running away” from the shoulders

Front of Release angle is too

Ring/Relese high/low

Keep right hand level with the ground and create proper release angle (~35 degrees) by leading with the hips, then opening the chest, and finally coming through with the right arm fast and last

Pole Vault Pole Drop Pole drop is too late/early

Walking drills that matches initiation and completion of pole drop with last strides of the approach

Pole Drop The athlete misses the

plant box with the pole

Sliding box drills allow the athlete to practice hitting the target without the risk inherent in doing so in the fixed box/pit

Swing The athlete doesn’t drop

shoulders

Training the athlete to become comfortable in an inverted position is critical.

Drilling this on a pull-up bar is perfect, where an athlete can practice moving from an upright to an inverted position, with hips higher than shoulders

Matthew Buns is an Assistant Cross Country and Track and Field Coach; Associate Professor of Kinesiology and Health Science, Concordia University, St. Paul, MN

Bar Clearance The athlete does not push

pole away from the bar,

and the pole knocks the

bar off

Once athlete inverts and then pikes over bar, push pole away with (right hand on top) right hand to keep pole from hitting bar

Tyler Naumowicz is a StudentAssistant Track and Field Coach at Concordia University, St. Paul, MN

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2017 Outdoor Track & Field National NCAA division i

Mike Holloway Florida Men’s Head COY

Petros Kyprianou Georgia Women’s Head COY

Tim Hall Tennessee Men’s Assistant COY

Curtis Taylor Oregon Women’s Assisstant COY

Christian Coleman Tennessee Men’s Track AOY

Raevyn Rogers Oregon Women’s Track AOY

Lindon Victor Texas A & M Men’s Field AOY

Maggie Ewen Arizona State Women’s Field AOY

Sandy Chapman Saint Augustine’s Men’s Assistant COY

Jennifer Michel Western State Women’s Assistant COY

Vincent Kiprop Missouri Southern Men’s Track AOY

Carly Muscaro Merrimack Women’s Track AOY

Cervantes Jackson Albany State Men’s Field AOY

Emilyn Dearman Pittsburg State Women’s Field AOY

Justin Kinseth Benedictine Men’s Assistant COY

Lane Lohr Washington Women’s Assistant COY

Parker Witt UW Whitewater Men’s Track AOY

Wadeline Jonathas UMass Boston Women’s Track AOY

Luke Winder North Central Men’s Field AOY

Alexa Wandy SUNY Geneseo Women’s Field AOY

NCAA division iiI

George Williams Saint Augustine’s Men’s Head COY

Darren Flowers West Texas A&M Women’s Head COY

NCAA division iIi

Josh Buchholtz UW La Crosse Men’s Head COY

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Jeff Stiles Washington Women’s Head COY

techniques AUGUST 2017

Coaches and Athletes of the Year NAIA

Laurier Primeau British Columbia Men’s Head COY

Brian Whitlock Wayland Baptist Women’s Head COY

Chris Johnson British Columbia Men’s Assistant COY

Ed McLaughlin Concordia Women’s Assistant COY

Jackson Thomas Bacone Men’s Track AOY

Anna Shields Point Park Women’s Track AOY

Zach Lurz Concordia Men’s Field AOY

Becky Collier Westmont Women’s Field AOY

NJCAA division I

Erik Vance South Plains Men’s Head COY

Keith Blackwill New Mexico Women’s Head COY

Willie Calvin Hinds Men’s Assistant COY

Jeff Becker New Mexico Women’s Assistant COY

Gilbert Kigen Central Arizona Men’s Track AOY

Susan Ejore Monroe Women’s Track AOY

Ricky Nelson Jr. Latavia Coombs Barton New Mexico Men’s Field AOY Women’s Field AOY

NJCAA division III Not Pictured

Dave Loobie Kingsborough Men’s Head COY Women’s Head COY

Joseph Kalnas Rowan Men’s Assistant COY

Rasheen Nicholson Kingsborough Women’s Assisstant COY

Stephen Cadogan Kingsborough Men’s Track AOY

Tatyana Mills Kingsborough Women’s Track AOY

Peter Flacco Rowan Men’s Field AOY

Keziann Jones Kingsborough Women’s Field AOY

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