College Football Overtime Outcomes: Implications for In-Game Decision-Making
The use of AI and machine learning in sports is increasingly prevalent, including their use for in-game strategy and tactics. This paper reports on the use of machine learning techniques, applying it to analysis of U.S. Division I-A College Football overtime games. The present overtime rules for tie...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-08-01
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| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/frai.2020.00061/full |
| _version_ | 1828866312495955968 |
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| author | Rick L. Wilson |
| author_facet | Rick L. Wilson |
| author_sort | Rick L. Wilson |
| collection | DOAJ |
| description | The use of AI and machine learning in sports is increasingly prevalent, including their use for in-game strategy and tactics. This paper reports on the use of machine learning techniques, applying it to analysis of U.S. Division I-A College Football overtime games. The present overtime rules for tie games in Division I-A college football was adopted in 1996. Previous research (Rosen and Wilson, 2007) found little to suggest that the predominantly used strategy of going on defense first was advantageous. Over the past decade, even with significant transformation of new offensive and defensive strategies, college football coaches still opt for the same conventional wisdom strategy. In revisiting this analysis of overtime games using both logistic regression and inductive learning/decision tree analysis, the study validates there remains no advantage to the defense first strategy in overtime. The study found evidence that point spread (as an indicator of team strength) and red zone offense performance of both teams were useful to predict game results. Additionally, by altering the decision-making “frame,” specific scenarios are illustrated where a coach can use these machine learning discovered relationships to influence end-of-regulation game decisions that may increase their likelihood of winning whether in regulation time or in overtime. |
| first_indexed | 2024-12-13T04:48:16Z |
| format | Article |
| id | doaj.art-73004bbd4ff248fca5c03c27e46afe95 |
| institution | Directory Open Access Journal |
| issn | 2624-8212 |
| language | English |
| last_indexed | 2024-12-13T04:48:16Z |
| publishDate | 2020-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj.art-73004bbd4ff248fca5c03c27e46afe952022-12-21T23:59:05ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122020-08-01310.3389/frai.2020.00061536651College Football Overtime Outcomes: Implications for In-Game Decision-MakingRick L. WilsonThe use of AI and machine learning in sports is increasingly prevalent, including their use for in-game strategy and tactics. This paper reports on the use of machine learning techniques, applying it to analysis of U.S. Division I-A College Football overtime games. The present overtime rules for tie games in Division I-A college football was adopted in 1996. Previous research (Rosen and Wilson, 2007) found little to suggest that the predominantly used strategy of going on defense first was advantageous. Over the past decade, even with significant transformation of new offensive and defensive strategies, college football coaches still opt for the same conventional wisdom strategy. In revisiting this analysis of overtime games using both logistic regression and inductive learning/decision tree analysis, the study validates there remains no advantage to the defense first strategy in overtime. The study found evidence that point spread (as an indicator of team strength) and red zone offense performance of both teams were useful to predict game results. Additionally, by altering the decision-making “frame,” specific scenarios are illustrated where a coach can use these machine learning discovered relationships to influence end-of-regulation game decisions that may increase their likelihood of winning whether in regulation time or in overtime.https://www.frontiersin.org/article/10.3389/frai.2020.00061/fullsportsfootballanalyticsmachine learningdecision making |
| spellingShingle | Rick L. Wilson College Football Overtime Outcomes: Implications for In-Game Decision-Making Frontiers in Artificial Intelligence sports football analytics machine learning decision making |
| title | College Football Overtime Outcomes: Implications for In-Game Decision-Making |
| title_full | College Football Overtime Outcomes: Implications for In-Game Decision-Making |
| title_fullStr | College Football Overtime Outcomes: Implications for In-Game Decision-Making |
| title_full_unstemmed | College Football Overtime Outcomes: Implications for In-Game Decision-Making |
| title_short | College Football Overtime Outcomes: Implications for In-Game Decision-Making |
| title_sort | college football overtime outcomes implications for in game decision making |
| topic | sports football analytics machine learning decision making |
| url | https://www.frontiersin.org/article/10.3389/frai.2020.00061/full |
| work_keys_str_mv | AT ricklwilson collegefootballovertimeoutcomesimplicationsforingamedecisionmaking |