TY - GEN
T1 - Are Sports Awards About Sports? Using AI to Find the Answer
AU - Shankar, Anshumaan
AU - Rajasekaran, Gowtham Veerabadran
AU - Hendricks, Jacob
AU - Schlak, Jared Andrew
AU - Sharma, Parichit
AU - K. R., Madhavan
AU - Kurban, Hasan
AU - Dalkilic, Mehmet M.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Sports awards have become almost as popular as the sports themselves bringing not only recognition, but also increases in salary, more control over decisions usually in the hands of coaches and general managers, and other benefits. Awards are so popular that even at the start of a season pundits and amateurs alike predict or argue for athletes. It is odd that something so apparently data-driven does not work in determining whether it is, indeed, data-driven. The simple question arises, “Are sports awards about sports?” Using ML (over a hundred potential models) this work aims to answer this question for professional basketball: Most Valuable Player, Most Improved Player, Rookie of the Year, and Defensive Player of the Year. Pertinent data is gathered including voting percentages. Our results are very interesting. MVP can be predicted well from the data, while the other three are more difficult. The findings suggest that either the data is insufficient (although no more sports data can be found) or more likely non-tangible factors are playing critical roles. This outcome is worth reflecting on for fans of all stripes: should sports awards be about sports? The source code along with instructions on running it to can be found in our github repository.
AB - Sports awards have become almost as popular as the sports themselves bringing not only recognition, but also increases in salary, more control over decisions usually in the hands of coaches and general managers, and other benefits. Awards are so popular that even at the start of a season pundits and amateurs alike predict or argue for athletes. It is odd that something so apparently data-driven does not work in determining whether it is, indeed, data-driven. The simple question arises, “Are sports awards about sports?” Using ML (over a hundred potential models) this work aims to answer this question for professional basketball: Most Valuable Player, Most Improved Player, Rookie of the Year, and Defensive Player of the Year. Pertinent data is gathered including voting percentages. Our results are very interesting. MVP can be predicted well from the data, while the other three are more difficult. The findings suggest that either the data is insufficient (although no more sports data can be found) or more likely non-tangible factors are playing critical roles. This outcome is worth reflecting on for fans of all stripes: should sports awards be about sports? The source code along with instructions on running it to can be found in our github repository.
KW - Award Prediction
KW - NBA
KW - Regression
KW - Sports Analytics
UR - https://www.scopus.com/pages/publications/85187701299
U2 - 10.1007/978-3-031-53833-9_8
DO - 10.1007/978-3-031-53833-9_8
M3 - Conference contribution
AN - SCOPUS:85187701299
SN - 9783031538322
T3 - Communications in Computer and Information Science
SP - 91
EP - 102
BT - Machine Learning and Data Mining for Sports Analytics - 10th International Workshop, MLSA 2023, Revised Selected Papers
A2 - Brefeld, Ulf
A2 - Davis, Jesse
A2 - Van Haaren, Jan
A2 - Zimmermann, Albrecht
PB - Springer Science and Business Media Deutschland GmbH
T2 - 10th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2023
Y2 - 18 September 2023 through 18 September 2023
ER -