陸上競技学会誌

第 19 巻第 1 号

2021［原著論文］

Determining Variables that will Predict Menʼs Shot Put Performancein the Qualifying Rounds of the Major Championships
Donald Babbitt1）
Abstract
This study obtained performance results for the 95 men’s shot
putters who participated in the major championships (Olympic
Games or International Association of Athletic Federations(IAAF) World Championships) between the years 2015-2017.
Statistical analyses were conducted for ten different variables(age, technique, seasonal best, personal best, major championships finals qualified for previously, average of three best competitions, average of five best competitions, average of three worst
competitions, average of best competitions within last 45 days
before the major championship, the average of the first three
throws of the competitions within the last 45 days before themajor championship) in order to determine which the variables
would best predict final round qualifying performance in a major
championship. Eight of the ten variables produced significant
correlations (at p <.01) for both qualifying round performanceand qualifying status. The strongest correlations were found tobe the personal best, t(93)= 6.41, p <.001, for whether an athlete would qualify/not qualify, and the average for the best competition results within the last 45 days before major championship (r =.745, p <.001) for performance in qualifying. Resultsfrom this study may be useful in guiding coaches, athletes, andfederations in their preparation for future major championships
in men’s shot put.

キーワード：shot put, Olympics, men, championship,prediction

Introduction

same way the World Cup is considered the major
championship for the sport of football (soccer). The winnerof these competitions is generally thought to be the best in
the world and will hold the title of Olympic Champion orWorld Champion until the next edition of that specific major
championship is contested.
Previous research has yielded many studies that haveexamined what performance metrics are associated withthe various aspects of throwing performance in the four
throwing disciplines (shot put, hammer, discus, and javelin).
Perhaps the most common has been the study of therelationship between throwing performance and weightlifting exercises (Judge et al., 2010; Judge et al., 2011;
Judge and Bellar, 2012; Pavlovic et al., 2012; Poprawski,
1988; Zaras et al, 2013; Zaras et al. 2014), or specificstrength exercises (Bondarchuk, 2007; Bondarchuk et al.,
1977; Karampatsos et al., 2017; Kyriazis et al., 2009;
Maszczyk et al., 2014; Zaras et al., 2019). Findings fromthese studies were able to determine correlations between
personal best performance and various training exercises
for an individual athlete, however, none of these findings

The shot put is a competitive track and field event that

addressed predicting performance in a specified competition

traces its origins back to “putting the stone” as part of theevent relative to the performance of other competitors in

Tailteann Games in Ireland, which took place between 1829

the same competition.

BC and 554 BC (Black, 2009). Historically, the event can

Numerous studies have also been conducted on analyzing

originally be described as throwing, or putting, a stone off

throwing performance by focusing their efforts on

the neck for distance. Currently, it is one of the four

describing the biomechanical components of men’s shot put

throwing events (shot put, discus, javelin, and hammer)

performance in order to explain a given performance basedthat is regularly contested in the event program in the sport

on the measurement of variables such as speed of release,of athletics. The men’s shot put event is contested byangle of release, and height of release (Ariel et al., 2005;

throwing a 7.26kg steel ball as far as possible while doing so

Ohyama-Byun et al., 2008; Dinsdale et al., 2018; Fenton etwithin the confines of a cement throwing circle whichal., 2012; Oh et al., 2011). In addition, comprehensive

measures exactly 7feet (2.135m) in diameter. The highest

biomechanical studies have been conducted on throwing

levels of competition for the sport of shot putting are

performance in major championships to assess moreconsidered to be the competitions at the Olympic Games

complex biomechanical variables ranging from torque andand the International Association of Athletics Federations

hip-shoulder separation readings to implement acceleration(IAAF) World Championships (Jampol, 2012 ; Mack, 2016).

patterns and flight characteristics (Badura, 2010; Bennett

These are considered the major championships in much the

et al., 2018; Dinsdale et al., 2018; Fenton et al., 2012;

1） University of Georgia
Athens, Georgia, USA11

Gutierrez et al., 2002; Isele and Nixdorf, 2010; Morriss etmajor championships, the swimming studies measured theal., 1997). As mentioned before, the bulk of these studies

effects of specific variables to quantify their effects on

have been designed to offer explanations for how a given

major championship performance as a whole. These

performance was achieved, yet, none of theseswimming studies were able to dig deeper into the basis forinvestigations were devised to predict future performance

major championship performance to look at theand placing relative to another competitor’s performancephysiological factors to predict performance rather than

who are of similar ability.

using pure statistical analysis from previous results toSpecific to track and field, there have been a number of

establish predictive trends.

studies that have attempted to predict future performance

To date, there have been very few studies to actually

results specific to the sports individual events by utilizing

examine the performances within the major championships

data collected from previous competition years and analysis

relative to performance in the qualifying rounds and itsof performance trends over time. These studies ultimate

potential impact for predicting performance in the final, oraim has been to predict future world record and maximal

medal round. In one of the few, Pavlovic and Idrizovic (2014)

human performance (Liu and Schutz, 1988), predict actual

conducted a study to determine the difference between

performance results for selected events in past and future

results between male and female javelin finalists at the

IAAF World Championships (Heazlewood and Walsh, 2015),

L ondon Olympic Games in 2012. The researchersand suggest mathematical models that describedattempted to see if the performances in the qualifying

performance result change for athletic events between

rounds were significantly related to results in the final

1960-2014 (Heazlewood and Walsh, 2015). A similar study

rounds for both genders. No statistical differences werefocusing on performance prediction by Pilianidis et al.

found for each gender’s performance from the qualifying(2012) used regression analysis to chronicle high prediction

rounds, however, it was observed that, surprisingly, onlyof performance accuracy in the men’s throwing events at

33％ of the competitors threw better in the final than in

the Mediterranean Games in 2013. With a comparable focus

qualifying. In contrast to these findings, Babbitt (2018)

on throwing performance prediction, Zhang et al. (2011)

found that performance in the qualifying rounds in theused document and mathematical statistics in an attempt tomajor championships in men’s hammer throw between

predict gold medal winning performance in the women’s

2008-2017 had the highest correlation (r = .6493, p <.01)

shot put in the 2012 Olympic Games, based on gold medal

with the results produced in the final or medal round.

performances from the previous five Olympic GamesThe purpose of this study was to identify the mostbetween 1992-2008. As with the case of Pilianidis et al.

important variables in predicting the ability of male shot(2012), the motivation for the study was to provide

putters to qualify out of the preliminary rounds into theinformation to guide a planning model for Chinese shot

final round of the last three major championships (IAAF

putters in preparation for the 2012 London Olympics. In

World Championships, or Olympic Games) in the sport ofretrospect, this study underestimated the winning

track and field. As can be seen from the review of

performance by nearly 70 cm, but with a subsequent doping

literature, there has yet to be a study performed on thisdisqualification for the winner, the gold medal result waslevel of inquiry as to what factors significantly influence theadjusted down to be off by only 33 cm.

ability to advance to final round in shot put’s highest level

The sport of swimming has also been the subject of majorof competition relative to other competitors in the samechampionship performance prognostication based on thecompetition. Given the high stakes nature of the Worldeffects of the time between performance peaks as well asChampionships and Olympic Games, and the amount of

the effects of various factor such as ranking at major events,

time and resources put into the development of thesestroke, race distance, sex, age, and country (Mujika et al.,competitors by national federations, it is believed that the2019), and the relationship between world ranking andresults of this study would be of immense benefit for

Olympic performance (Trewin et al., 2004). Similarly,

coaches, athletes, high performance directors, and other

Anderson et al. (2008) attempted to quantif y the

stakeholders in order to select the best athletes andrelationship between changes in test measures, such as

develop the best competition and performance strategy fors k i n f o l d a n a l y s i s , s t ro ke r a t e , a n d b l o o d l a c t a t etheir ultimate success. It was hypothesized that factors

concentration, and changes in performance at major

related to seasonal performance results, previous qualifying

competitions for elite swimmers. However, unlike thesuccess, age, and technique would be statistically significant

studies into track and field performance prediction at thepredictors of major championship performance. Given that12

陸上競技学会誌

the variables all displayed significance, the secondary aimof this study was to determine the order of significance forthese variables relative to qualifying status, and preliminary
round performance.

第 19 巻第 1 号

results for a given season.
1
X5best =(x1 + x2 + x3 + x4 + x5)
5
X3worst =1(y1 + y2 + y3)
3

2021

(2)
(3)

Methods

The average for the best competition results within the

The study obtained performance results from the 95

last 45 days before major championship (X45days) for the firstmen’s shot putters who par ticipated in the major

three throws of the competitions within the last 45 days

championships (Olympic Games and/or IAAF World

before the major championship (X 3/45days) were calculated

Championships) between the years of 2015-2017. The

using the following equation were Σ (x1…..xn) represents

performance data was derived from competition results

the sum of the competition results within the last 45 days

from both the official IAAF (n.d.) and Tilastopaja (n.d.)leading up to the major championship:1
X3/45days or X45days =Σ (x1…..xn)n

websites. Data for each athlete who competed in thequalifying rounds of these competitions was recorded forthe following variables:(4)

All data having to do with distance in meters was scaled

1.

Best mark achieved in qualification for a given major

by dividing the performance results (in meters) by thechampionshipautomatic qualifying standard for the major championship2.

qualification status (whether they qualified for thefrom that given season in meters. Table 1 lists the

major championship final or not)automatic qualifying standard needed to achieve automatic

type of shot put technique utilized (rotational orentry into the major championship for a given season.

glide)

These standards were used to scale the performances,

4.

age

which would be expressed as a percentage, in order to5.

number of times previously qualifying through to aestablish a base scale to equate results from year to year.

major championship final round (MCFQ) (major

Dependent of the nature of the variables, a calculation ofchampionships are defined at Olympic Games, IAAF

either Pearson’s correlation coefficient, a Chi-square test,

World Outdoor Championships, and IAAF Worldor independent T-test was applied

3.

Indoor Championships)
6.

all-time personal best (PB)

Table 1.

7.

seasonal best for that given year (SB)The Automatic Qualifying Standard Needed for Entry into8.

the average for the three best competition results for

Each Major Championship from 2015-2017,that given year (X3best)
9.

2015 IAAF World Championships

Automatic Qualifying
Standard
20.45m2016 Olympic Games2017 IAAF World Championships

20.50m20.50mMajor Championshipthe average for the five best competition results forthat given year (X5best)10.

the average for the best competition results within

11.

the average for the best competition results for thefor 10 different variables (age, technique, MCQF, PB, SB,

first three throws of the competitions within the last

X3best, X5best, X45days, X3/45days, X3worst) in relation to performance

45 days before the major championship (X3/45days)in the qualifying rounds, and athlete qualifying status for

The average of the three worst competition results

the final round were performed. A current statistical

for that given year (X3worst)

software package (IBM SPSS Statistics Version 25.0) was

the last 45 days before major championship (X45days)

12.

The best three-meet average (X3best) was calculated by
dividing the sum of the three best competition results (x1,x2, x3) for a given season by the number of competitions(three) as shown in the following equation:
1
Xbest =
(x1 + x2 + x3)
3(1)

used to perform the various analyses and statisticalsignificance was set at p < .05.

Results
The statistical analyses for ten different variables (age,technique, MCQF, PB, SB, X3best, X5best, X45days, X3/45days, X3worst)

A similar method was used to calculate the best five-

revealed that all variables with the exception of age andmeet average (X5best), and worst three-meet average (X3worst)

technique produced statistically significant results inwith (y1, y2, y3) representing the three worst competition

relation to performance of the athletes in the qualifying

13

round and relative to whether the athlete was able to qualify

Table 3.

(by finishing in the top 12 places) for the final round. The

Correlation Coefficients Between Selected Variables Relative toresults of these t statistic were reported in Table 2 relative

Men’s Shot Put Performance in the Qualifying Rounds in theto whether the athlete was able to qualify (qualifying status)

Major Championships Between 2015-2017.

for the final round, and Table 3 for the Pearson’s correlation

Relationship of correlation

coefficient (r), relative to the athlete’s performance in the

Performance vs X45days
Performance vs X3best
Performance vs X3worst
Performance vs PB
Performance vs SB
Performance vs X3/45days
Performance vs MCFQ
Performance vs X5best
Performance vs age

qualifying round (qualifying performance). Results werelisted in descending order in both tables from strongest to
weakest t statistic or correlation. The variable with thehighest impact on whether an athlete would qualify for the
final round was that of the athlete’s all-time personal best
performance, t(93)= 6.41, p < .001. There variable with thehighest correlation to performance in the qualifying round
was found to be the average per for mance in thecompetitions within the 45 days (X45days) leading up to themajor championship (r =.745, p <.001).
Table 2.
T-Scores for Selected Variables Related to Men’s Shot Put
Performance in the Qualifying Rounds Relative to Whether they

r

R2

N

Significance

.745
.698
.696
.654
.644
.638
.324
.223
-.103

.555
.487
.484
.427
.414
.407
.104
.049
.010

87
94
94
94
94
85
94
93
94

p < .01
p <.01
p <.01
p <.01
p <.01
p <.01
p <.01
p <.05not sig.

Note: X45days: the average for the best competition results within
the last 45 days before major championship; PB: all-time personal best; X3/45days: the average for the best competition results
for the first three throws of the competitions within the last 45
days before the major championship; SB: seasonal best for thatgiven year; X3best: the average for the three best competition
results for that given year; X5best: the average for the five bestcompetition results for that given year; MCFQ: number of times
previously qualifying through to a major championship final
round; X3worst: the average of the three worst competition results
for that given year.

Qualified for the Final Round in the Major Championships

The bottom two variables (technique and age) did not

Between 2015-2017.

display statistical significance in either table. Conversely,

Relationship of correlation
Qualified vs PB
Qualified vs X45days
Qualified vs X3worst
Qualified vs X3best
Qualified vs X3/45days
Qualified vs SB
Qualified vs X5best
Qualified vs MCFQ
Qualified vs age

df
93
86
93
93
84
93
92
93
93

t statistic

Significancethe average of the five best competitions (X5best) was weakly

6.41
6.17
5.76
5.56
5.52
5.20
4.48
4.42
-0.03

p < .001
p <.001
p <.001
p <.001
p <.001
p <.001
p <.001
p <.001
not sig.

significant (r =.223, p <.05) relative to performance in the

Note: X45days: the average for the best competition results within
the last 45 days before major championship; PB: all-time personal best; X3/45days: the average for the best competition results
for the first three throws of the competitions within the last 45
days before the major championship; SB: seasonal best for thatgiven year; X3best: the average for the three best competition
results for that given year; X5best: the average for the five bestcompetition results for that given year; MCFQ: number of times
previously qualifying through to a major championship final
round; X3worst: the average of the three worst competition results
for that given year.

qualifying rounds, while displaying statistical significancewith regard to the athletes qualifying status, t(92) = 4.48,
p <.001. In order to determine if there was a statisticaldifference between the rotational or glide technique withregard to qualifying status or qualifying performance, aT-test was utilized to determine differences in qualifying
performance based on the technique used, while a Chisquare analysis was used to determine differences between
qualifying status and the specific shot put technique thatwas utilized. In both cases, no significant differences were
found between qualifying performance and techniquet(92)= 1.15, p = .1258, or qualifying status and technique
X 2(1, 95)= .47, p = .490.

There were also strong correlations found between both

Discussion

qualifying performance and whether the athlete qualified

The primary purpose of this study was to identify thefor the final round and the variables X3best, X3/45, X3worst, PB,

variables of elite male shot put performance that could best

X5best, MCFQ, SB, and X45days. However, despite have thepredict the performance of these athletes in the qualifying

same variables showing high levels of statisticalrounds at the major championships for the sport of athletics.

significance, the correlating variables did not exhibit the

In order to do this, performance data for the competition

same descending order pattern in both tables (see Tables 2

year leading up to the major championship was collected forand 3).

each athlete that qualified and participated in each major
championship held from 2015-2017. Applicable statisticalmethods were used for each of the variables under study to
see which ones were the most important in predicting

14

陸上競技学会誌

第 19 巻第 1 号

2021

whether an athlete would qualify for the final round of thethe last 45 days before the major championship (X3/45days),major championship.

and average of the three worst competition results for that

Statistical analysis revealed that nearly all of the variables

given year (X3worst)), were all highly significant indicators ofdisplayed some level of statistical significance. Eight out ofa shot putter’s ability and performance potential. The

ten variables in relation to both qualif ying round

specific measure of the shot putters worst three

performance and qualifying status met the threshold for

performances (X3worst) was considered to be an measure ofstatistical significance set at p <. 05. The average

how well the athlete would perform under pressure since itcompetition performance within the forty-five days leading

was a representation of what they would do on their worst

up to the major championship (X45days) exhibited the highestday. Qualifying rounds can often devolve into situations

correlation of all the variables for performance in qualifying,

where a competitor is trying to survive to advance to ther =.745, p <.01, and was the second highest with regard to

final round rather than maximizing their best form

qualifying status, t(86)= 6.17, p <.011. According to Cohen

according to University of Southern California Throws(1988) this Pearson’s correlation coefficient associated with

Coach Dan Lange (personal communication, June 3rd, 2019).

qualifying performance represented a very high correlation.

The one remaining variable that was ranked seventh inThis finding suggested that level of competitive fitness an

the descending order list in qualifying round performanceathlete exhibits going into the major championship may be

was a measure of the competitors experience that counted

the best indicator of potential performance. While it

the number of times previously qualifying through to aappeared pretty straightforward that producing strong

major championship final round (MCFQ). While thiscompetitive performances leading up to an importantvariable did exhibit a mild correlation (r between .3 and .5)

competition would lead to similar results in the important

it was conjectured that it was not a stronger predictor since

competition, there may have been other added benefits toolder more experienced competitors with a high value forstrong lead-up performances in addition to just exhibiting a

MCFQ may have also been past their prime and lesshigh level of competitive fitness. Strong lead-up

prepared physically than their younger competitors, thus

performances going into major competitions may havenegating some of the positive effects of successfulenhanced the competitor’s confidence that they would do

experience. This can be explained by the inverted-U

well in competitions of even greater significance. This

character of the age-performance curve for men’s shot

development could have built upon the competitors alreadyputters which crests at 27.8 years of age according tostrong level of performance. Rosenqvist and Skans (2015)

Haugen et al (2018) and 27.3 years of according to Babbitt

found that previous success is an important prerequisite

and Saatara (2014), interacting with the linear nature of thew h e n p e r f o r m i n g i n h i g h - s t a ke s c o m p e t i t i o n s i nmajor championship qualification experience which isprofessional golfers. These observations may have carried

cumulative over time.

over to the sports such as shot put whose qualification

The variables of both age and type of shot put techniqueformat in major competitions is similar in many ways to(glide and rotational) did not demonstrate statisticalthat of making the cut in golf. It was also postulated that ansignificance. Both techniques have been used successfullyenhanced sense of hope or confidence brought about by

over time at the major championships. While the rotational

successful performances leading into an important

technique has been gaining popularity over the past fifteencompetition could also explain why qualifying performance

years and is now the predominant technique used by men’s

was most highly correlated with athlete performance in the

shot putters at the elite level (Babbitt and Byun, 2017), it

45 days leading into the major championship (Curry et al.,does not diminish the effectiveness of the glide technique

1997).

which has been equally successful and has won many

Five other variables exhibited a strong correlation tochampionship medals over the years. The average of thequalifying performance with correlation coefficients of r <five best competitions (X5best) was the only variable that did.60, however, they did appear in a different descending

display weak significance (r =.223, p <.05) in relation toorder in relation to qualif ying status and qualif yingqualifying performance, however, it was found to be

performance (see Tables 2 and 3). Nonetheless, thesesignificant relative to qualifying status, t(92)= 4.48,

performance driven variables (personal best (PB), seasonal

p <.001. The most likely cause for the low correlation tobest (SB), average for the three best competition results for

qualifying performance was that this specific variable tookthat given year (X3best), average for the best competition

into account too many competition results to make a clear

results for the first three throws of the competitions within

distinction between shot putters performance during the15

competitive season. Given this result, it appears thatI n . Q u i n g Wa n g ( E d . ) P r o c e e d i n g f r o m t h e 2 3 r daveraging on the top or bottom three results for wholeInternational Symposium on Biomechanics in Sports,

competition year before the major championship was thePaper presented at ISBS, Beijing, China, August 22-27,

more effective way to predict qualifying performance from2005 (pp. 271-274).

in-season results.

Babbitt, D., and Saatara, M. (2014). Elite level development

Conclusions

rates and age-based performance patterns for the men’s
throwing events. New Studies in Athletics, 29(3/4), 69-75.

In conclusion, this investigation showed that the variables

Babbitt, D., Byun K. (2017). The basic technique ofof average competition performance within the forty-fiverotational shot put. Track and Field Monthly, 2017(4),days leading up to the major championship (X45days), personal

150-154. (Japanese language)

best (PB), seasonal best (SB), average for the three best

Babbitt, D. (2018). A trend analysis of major championshipcompetition results for that given year (X3best), average forresults in male hammer throw (2008-2017), Asian Journal

the best competition results for the first three throws of theof Coaching Science, 2(1), 1-11.

competitions within the last 45 days before the major

Badura, M. (2010). Biomechanical analysis of the discus atchampionship (X 3/45days), and average of the three worst

the 2009 IAAF world championships In athletics. New

competition results for that given year (X3worst) all displayed

Studies in Athletics, 25(3), 23-35

a high correlation with regard to predicting performance inBennett, T., Walker, J., and Bissas, A. (2018). Biomechanicalmajor championship qualif ying for men’s shot put.

report for the IAAF world championships London 2017

Furthermore, the variable measuring the number of times

discus throw men’s. Monaco, International Association ofpreviously qualifying through to a major championship final

Athletic Federations.

round (MCFQ) also displayed a mild correlation. These

Black, C. (2009). History of the Scottish heavy events.

majority of these results were in line with the hypothesis

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predicting qualifying performance in the sport of track andfield’s major championships when competing against other

Bondarchuk, A. P. (2007). Transfer of training in sport.
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Bondarchuk, A., Ivanova, L., and Vinnitchuk, W. (1977).

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Curry, L. A., Snyder, C. R., Cook, D. L., Ruby, B. C., andperformance within the timeframe. Given these findings, all

Rehm, M. (1997). Role of hope inm a j o r s t a ke h o l d e r s i n v o l v e d i n t h e s u p p o r t a n dachievement. Journal of Personality and Social Psychology,

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Dinsdale, A., Thomas, A., and Bissas, A. ( 2018).

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for the gold-medal