Relationships between contract status and player performance in the Australian Football League.

This study analysed the extent to which player performance differs within the Australian Football League (AFL) with respect to the status of a player's contract. AFL Player Ratings (AFLPR) and contract data were obtained during the 2013-2020 AFL seasons for all 827 players listed by an AFL club at the beginning of the 2020 season. A model of "expected performance" was created allowing for an exploration into the differential with actual performance as a function of contract status. Paired t-tests indicated that there was a difference in performance pre- and post-signing their contract for players who signed mid-season (mean change and 95% confidence interval of -1.48 ± 0.93 and -0.49 ± 0.48 AFLPR, at ten match intervals for those in- and out-of-contract at the conclusion of that year's season, respectively). Further differences existed between the groups of players who signed mid-season, as compared to those who signed during the off-season. Correlation analyses indicated that more consistent performers are somewhat less likely to see a reduction in performance post signing as compared to less consistent performers. The applications of these findings have the potential to support organisational decisions relating to the timing and nature of player contracting.

Apples and oranges? Comparing player performances between the Australian Football League and second-tier leagues.

This study developed a model to determine the extent to which player performance objectively differs between various Australian football (AF) leagues. Champion Data (CD) ranking points were obtained during the 2016-2019 seasons, for all players across the Australian Football League (AFL) and the 10 main second-tier AF leagues. Data pertaining to each player's age, playing position and the AF leagues in which they competed in were also collected. Phase One investigated the difference between the AFL and the senior second-tier leagues in which AFL affiliate teams participate. Post-hoc tests indicated that objective player performance was substantially different between the AFL and each of the four senior second-tier leagues (effects ranging from 16.8 to 21.6 CD ranking points). Phase Two investigated the difference between the second-tier leagues from which players are traditionally drafted by an AFL club. Post-hoc tests indicated that objective player performance was substantially different between the four senior second-tier leagues as well as the under-18 national championships, in comparison to each of the reserve and under-18 state leagues. Professional sporting organisations may utilise the methods provided here as an example of what could be implemented to support decisions regarding player contracting, recruitment and team selection.

A method to assess the influence of individual player performance distribution on match outcome in team sports.

This study developed a method to determine whether the distribution of individual player performances can be modelled to explain match outcome in team sports, using Australian Rules football as an example. Player-recorded values (converted to a percentage of team total) in 11 commonly reported performance indicators were obtained for all regular season matches played during the 2014 Australian Football League season, with team totals also recorded. Multiple features relating to heuristically determined percentiles for each performance indicator were then extracted for each team and match, along with the outcome (win/loss). A generalised estimating equation model comprising eight key features was developed, explaining match outcome at a median accuracy of 63.9% under 10-fold cross-validation. Lower 75th, 90th and 95th percentile values for team goals and higher 25th and 50th percentile values for disposals were linked with winning. Lower 95th and higher 25th percentile values for Inside 50s and Marks, respectively, were also important contributors. These results provide evidence supporting team strategies which aim to obtain an even spread of goal scorers in Australian Rules football. The method developed in this investigation could be used to quantify the importance of individual contributions to overall team performance in team sports.

Deferring draft picks: Empirical analysis of the AFL draft.

Trading picks is a common transaction observed in sporting leagues that employ player drafts. However, many current and revised draft formats employed by professional leagues allow for the trading of picks in the current year for those in the future. This in turn raises a question of an appropriate rate of discount which can be used to account for the delay in relinquishing current picks for those in the future. This study proposes a discount rate function in the Australian Football League based on the difference between a player's contribution to his team in his first season to that of his career. The findings reveal a varying discount rate that decreases with the progression of the draft ranging between 42% and 51%, given the increasing deviation of post draft outcomes in the same range. Furthermore, after discounting said picks for four years, the variance in pick reduced considerably as poor performers are delisted after their initial contract period. Upon adopting the model to evaluate previous trades, it is evident that almost all parties infer a higher value on current year selections than future selections (discounting future picks by up to 120%), seeking instant returns over deferred options. Hence, a decision maker trying to capitalise on pick value only would be better off deferring the right to choose in the current year.

Compounding endowment effects when trading draft picks in the Australian Football League.

Endowment effect relates to a situation when decision makers are more likely to retain an object they own, than acquire the same object when they do not own it. Studies have often concluded that players recruited early on through drafts are more likely to be held in team rosters irrespective of their marginal utility. We tested the hypothesis wherein this effect would compound when the pick used to select a player is traded between teams. Using a sample of draftees selected between 2003 and 2016 in the Australian Football League, we created a proportional hazard model to predict the career longevity of a player with their drafting team and overall career. The results suggest each subsequent trade marginally reduced the exit of a player by a log normal rate of 0.269 in their career with the team that initially drafted them. The findings were attributed to the premium requested by the original team that is compounded with every exchange as the reference points used to determine value have also shifted with the trade.

Optionality in Australian Football League draftee contracts.

Though player drafts have commonly been utilised to equitably disperse amateur talent and avoid bidding wars, often they have also been accused of creating a monopsony labour market which restricts player movement. Within the Australian Football League (AFL) some have called for the increase of the initial draftee contract from two to three seasons, which further pushes the envelope on monopsony power. Instead of increasing the contract length, this paper suggests a call option to be purchased by the teams allowing them to add a further season to the draftee contract at a predetermined compensation package should they choose to do so at the end of the initial contract. The call prices per pick were calculated using the Black-Scholes model and were valued between 1% and 1.5% of the pick value. However, it failed to follow a monotonic function similar to pick value, owing to managerial overconfidence and sunk investment plays. Overall, the findings allow teams to procure the option of increasing initial draftee contracts and not impede further on a player's ability to move.

Multifactorial Benchmarking of Longitudinal Player Performance in the Australian Football League.

This study aimed to develop a model to objectively benchmark professional Australian Rules football (AF) player performance based on age, experience, positional role and both draft type and round in the Australian Football League (AFL). The secondary aims were to identify the stage of peak performance and specific breakpoints in AF player performance longitudinally. AFL Player Ratings data were obtained for all players (n = 1052) from the 1034 matches played during the 2013-2017 seasons, along with data pertaining to the abovementioned player characteristics. Two separate linear mixed models revealed that all factors influenced player performance, with age and experience the strongest in each model, respectively. Post hoc Tukey tests indicated that performance was affected by age at each level up until the age of 21 (effect ranging from 0.98 to 3.70 rating points), and by experience at the levels 1-20 and 21-40 matches in comparison to all higher levels of experience (effect ranging from 1.01 to 3.77 rating points). Two segmented models indicated that a point of marginal gains exists within longitudinal performance progression between the age levels 22 and 23, and the experience levels 41-60 and 61-80 matches. Professional sporting organisations may apply the methods provided here to support decisions regarding player recruitment and development.

Comparing subjective and objective evaluations of player performance in Australian Rules football.

Player evaluation plays a fundamental role in the decision-making processes of professional sporting organisations. In the Australian Football League, both subjective and objective evaluations of player match performance are commonplace. This study aimed to identify the extent to which performance indicators can explain subjective ratings of player performance. A secondary aim was to compare subjective and objective ratings of player performance. Inside Football Player Ratings (IFPR) and Australian Football League Player Ratings were collected as subjective and objective evaluations of player performance, respectively, for each player during all 1026 matches throughout the 2013-2017 Australian Football League seasons. Nine common player performance indicators, player role classification, player age and match outcomes were also collected. Standardised linear mixed model and recursive partitioning and regression tree models were undertaken across the whole dataset, as well as separately for each of the seven player roles. The mixed model analysis produced a model associating the performance indicators with IFPR at a root mean square error of 0.98. Random effects accounting for differences between seasons and players ranged by 0.09 and 1.73 IFPR each across the five seasons and 1052 players, respectively. The recursive partitioning and regression tree model explained IFPR exactly in 35.8% of instances, and to within 1.0 IFPR point in 81.0% of instances. When analysed separately by player role, exact explanation varied from 25.2% to 41.7%, and within 1.0 IFPR point from 70.3% to 88.6%. Overall, kicks and handballs were most associated with the IFPR. This study highlights that a select few features account for a majority of the variance when explaining subjective ratings of player performance, and that these vary by player role. Australian Football League organisations should utilise both subjective and objective assessments of performance to gain a better understanding of the differences associated with subjective performance assessment.