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Volume 15 (2016): Issue 1 (July 2016)

Journal Details
Format
Journal
eISSN
1684-4769
First Published
16 Apr 2016
Publication timeframe
2 times per year
Languages
English

Search

Volume 15 (2016): Issue 2 (December 2016)

Journal Details
Format
Journal
eISSN
1684-4769
First Published
16 Apr 2016
Publication timeframe
2 times per year
Languages
English

Search

3 Articles
Open Access

Predicting ratings of perceived exertion in Australian football players: methods for live estimation

Published Online: 17 Dec 2016
Page range: 64 - 77

Abstract

Abstract

The ability of machine learning techniques to predict athlete ratings of perceived exertion (RPE) was investigated in professional Australian football players. RPE is commonly used to quantifying internal training loads and manage injury risk in team sports. Data from global positioning systems, heart-rate monitors, accelerometers and wellness questionnaires were recorded for each training session (n=3398) from 45 professional Australian football players across a full season. A variety of modelling approaches were considered to investigate the ability of objective data to predict RPE. Models were compared using nested cross validation and root mean square error (RMSE) on RPE predictions. A random forest model using player normalised running and heart rate variables provided the most accurate predictions (RMSE ± SD = 0.96 ± 0.08 au). A simplification of the model using only total distance, distance covered at speeds between 18-24 km·h−1, and the product of total distance and mean speed provided similarly accurate predictions (RMSE ± SD = 1.09 ± 0.05 au), suggesting that running distances and speeds are the strongest predictors of RPE in Australian football players. The ability of non-linear machine learning models to accurately predict athlete RPE has applications in live player monitoring and training load planning.

Keywords

  • GPS
  • RPE
  • machine learning
  • training load
Open Access

A Rating System For Gaelic Football Teams: Factors That Influence Success

Published Online: 17 Dec 2016
Page range: 78 - 90

Abstract

Abstract

AIM: The current investigation aimed to create an objective rating of Gaelic football teams and to examine factors relating to a team's rating. METHOD: A modified version of the Elo Ratings formula (Elo, 1978) was used to rate Gaelic football teams. A total of 1101 competitive senior Inter County matches from 2010-2015 were incorporated into calculations. Factors examined between teams included population, registered player numbers, previous success at adult and underage levels, financial income from the GAA, team expenses and number of clubs in a county. RESULTS: The Elo Ratings formula for Gaelic football was found to have a strong predictive ability, correctly predicting the result in 72.90% of 642 matches over a 6 year period. Strong positive correlations were observed between previous success at senior level, Under 21 level, Under 18 level and current Elo points. Moderate correlations exist between population figures and current Elo points. Moderate correlations are also evident between the number of registered players in a county and the county’s Elo rating points. CONCLUSION: Gaelic football teams can be objectively rated using a modified Elo Ratings formula. In order to develop a successful senior team, counties should focus on the development of underage players, particularly up to U18 and U21 level.

Keywords

  • Gaelic Football
  • Elo Rating System
  • Success
  • Senior Success
  • Underage Success
Open Access

Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods

Published Online: 17 Dec 2016
Page range: 91 - 112

Abstract

Abstract

Baseball is a statistically filled sport, and predicting the winner of a particular Major League Baseball (MLB) game is an interesting and challenging task. Up to now, there is no definitive formula for determining what factors will conduct a team to victory, but through the analysis of many years of historical records many trends could emerge. Recent studies concentrated on using and generating new statistics called sabermetrics in order to rank teams and players according to their perceived strengths and consequently applying these rankings to forecast specific games. In this paper, we employ sabermetrics statistics with the purpose of assessing the predictive capabilities of four data mining methods (classification and regression based) for predicting outcomes (win or loss) in MLB regular season games. Our model approach uses only past data when making a prediction, corresponding to ten years of publicly available data. We create a dataset with accumulative sabermetrics statistics for each MLB team during this period for which data contamination is not possible. The inherent difficulties of attempting this specific sports prediction are confirmed using two geometry or topology based measures of data complexity. Results reveal that the classification predictive scheme forecasts game outcomes better than regression scheme, and of the four data mining methods used, SVMs produce the best predictive results with a mean of nearly 60% prediction accuracy for each team. The evaluation of our model is performed using stratified 10-fold cross-validation.

Keywords

  • Major League Baseball
  • Sabermetrics
  • Data Mining
  • Prediction
  • Classification
  • Regression
3 Articles
Open Access

Predicting ratings of perceived exertion in Australian football players: methods for live estimation

Published Online: 17 Dec 2016
Page range: 64 - 77

Abstract

Abstract

The ability of machine learning techniques to predict athlete ratings of perceived exertion (RPE) was investigated in professional Australian football players. RPE is commonly used to quantifying internal training loads and manage injury risk in team sports. Data from global positioning systems, heart-rate monitors, accelerometers and wellness questionnaires were recorded for each training session (n=3398) from 45 professional Australian football players across a full season. A variety of modelling approaches were considered to investigate the ability of objective data to predict RPE. Models were compared using nested cross validation and root mean square error (RMSE) on RPE predictions. A random forest model using player normalised running and heart rate variables provided the most accurate predictions (RMSE ± SD = 0.96 ± 0.08 au). A simplification of the model using only total distance, distance covered at speeds between 18-24 km·h−1, and the product of total distance and mean speed provided similarly accurate predictions (RMSE ± SD = 1.09 ± 0.05 au), suggesting that running distances and speeds are the strongest predictors of RPE in Australian football players. The ability of non-linear machine learning models to accurately predict athlete RPE has applications in live player monitoring and training load planning.

Keywords

  • GPS
  • RPE
  • machine learning
  • training load
Open Access

A Rating System For Gaelic Football Teams: Factors That Influence Success

Published Online: 17 Dec 2016
Page range: 78 - 90

Abstract

Abstract

AIM: The current investigation aimed to create an objective rating of Gaelic football teams and to examine factors relating to a team's rating. METHOD: A modified version of the Elo Ratings formula (Elo, 1978) was used to rate Gaelic football teams. A total of 1101 competitive senior Inter County matches from 2010-2015 were incorporated into calculations. Factors examined between teams included population, registered player numbers, previous success at adult and underage levels, financial income from the GAA, team expenses and number of clubs in a county. RESULTS: The Elo Ratings formula for Gaelic football was found to have a strong predictive ability, correctly predicting the result in 72.90% of 642 matches over a 6 year period. Strong positive correlations were observed between previous success at senior level, Under 21 level, Under 18 level and current Elo points. Moderate correlations exist between population figures and current Elo points. Moderate correlations are also evident between the number of registered players in a county and the county’s Elo rating points. CONCLUSION: Gaelic football teams can be objectively rated using a modified Elo Ratings formula. In order to develop a successful senior team, counties should focus on the development of underage players, particularly up to U18 and U21 level.

Keywords

  • Gaelic Football
  • Elo Rating System
  • Success
  • Senior Success
  • Underage Success
Open Access

Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods

Published Online: 17 Dec 2016
Page range: 91 - 112

Abstract

Abstract

Baseball is a statistically filled sport, and predicting the winner of a particular Major League Baseball (MLB) game is an interesting and challenging task. Up to now, there is no definitive formula for determining what factors will conduct a team to victory, but through the analysis of many years of historical records many trends could emerge. Recent studies concentrated on using and generating new statistics called sabermetrics in order to rank teams and players according to their perceived strengths and consequently applying these rankings to forecast specific games. In this paper, we employ sabermetrics statistics with the purpose of assessing the predictive capabilities of four data mining methods (classification and regression based) for predicting outcomes (win or loss) in MLB regular season games. Our model approach uses only past data when making a prediction, corresponding to ten years of publicly available data. We create a dataset with accumulative sabermetrics statistics for each MLB team during this period for which data contamination is not possible. The inherent difficulties of attempting this specific sports prediction are confirmed using two geometry or topology based measures of data complexity. Results reveal that the classification predictive scheme forecasts game outcomes better than regression scheme, and of the four data mining methods used, SVMs produce the best predictive results with a mean of nearly 60% prediction accuracy for each team. The evaluation of our model is performed using stratified 10-fold cross-validation.

Keywords

  • Major League Baseball
  • Sabermetrics
  • Data Mining
  • Prediction
  • Classification
  • Regression

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