Goal or Miss? A Bernoulli Distribution for In-Game Outcome Prediction in Soccer
Due to a colossal soccer market, soccer analysis has attracted considerable attention from industry and academia. In-game outcome prediction has great potential in various applications such as game broadcasting, tactical decision making, and betting. In some sports, the method of directly predicting...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/7/971 |
| _version_ | 1797433591559356416 |
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| author | Wendi Yao Yifan Wang Mengyao Zhu Yixin Cao Dan Zeng |
| author_facet | Wendi Yao Yifan Wang Mengyao Zhu Yixin Cao Dan Zeng |
| author_sort | Wendi Yao |
| collection | DOAJ |
| description | Due to a colossal soccer market, soccer analysis has attracted considerable attention from industry and academia. In-game outcome prediction has great potential in various applications such as game broadcasting, tactical decision making, and betting. In some sports, the method of directly predicting in-game outcomes based on the ongoing game state is already being used as a statistical tool. However, soccer is a sport with low-scoring games and frequent draws, which makes in-game prediction challenging. Most existing studies focus on pre-game prediction instead. This paper, however, proposes a two-stage method for soccer in-game outcome prediction, namely in-game outcome prediction (IGSOP). When the full length of a soccer game is divided into sufficiently small time frames, the goal scored by each team in each time frame can be modeled as a random variable following the Bernoulli distribution. In the first stage, IGSOP adopts state-based machine learning to predict the probability of a scoring goal in each future time frame. In the second stage, IGSOP simulates the remainder of the game to estimate the outcome of a game. This two-stage approach effectively captures the dynamic situation after a goal and the uncertainty in the late phase of a game. Chinese Super League data have been used for algorithm training and evaluation, and the results demonstrate that IGSOP outperforms existing methods, especially in predicting draws and prediction during final moments of games. IGSOP provides a novel perspective to solve the problem of in-game outcome prediction in soccer, which has a potential ripple effect on related research. |
| first_indexed | 2024-03-09T10:19:11Z |
| format | Article |
| id | doaj.art-4f4206305eac42018beb6957eed8882d |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T10:19:11Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-4f4206305eac42018beb6957eed8882d2023-12-01T22:06:57ZengMDPI AGEntropy1099-43002022-07-0124797110.3390/e24070971Goal or Miss? A Bernoulli Distribution for In-Game Outcome Prediction in SoccerWendi Yao0Yifan Wang1Mengyao Zhu2Yixin Cao3Dan Zeng4Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Institute of Advanced Communication and Data Science, Shanghai University, Shanghai 200444, ChinaKey Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Institute of Advanced Communication and Data Science, Shanghai University, Shanghai 200444, ChinaKey Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Institute of Advanced Communication and Data Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Science, Fudan University, Shanghai 200433, ChinaKey Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Institute of Advanced Communication and Data Science, Shanghai University, Shanghai 200444, ChinaDue to a colossal soccer market, soccer analysis has attracted considerable attention from industry and academia. In-game outcome prediction has great potential in various applications such as game broadcasting, tactical decision making, and betting. In some sports, the method of directly predicting in-game outcomes based on the ongoing game state is already being used as a statistical tool. However, soccer is a sport with low-scoring games and frequent draws, which makes in-game prediction challenging. Most existing studies focus on pre-game prediction instead. This paper, however, proposes a two-stage method for soccer in-game outcome prediction, namely in-game outcome prediction (IGSOP). When the full length of a soccer game is divided into sufficiently small time frames, the goal scored by each team in each time frame can be modeled as a random variable following the Bernoulli distribution. In the first stage, IGSOP adopts state-based machine learning to predict the probability of a scoring goal in each future time frame. In the second stage, IGSOP simulates the remainder of the game to estimate the outcome of a game. This two-stage approach effectively captures the dynamic situation after a goal and the uncertainty in the late phase of a game. Chinese Super League data have been used for algorithm training and evaluation, and the results demonstrate that IGSOP outperforms existing methods, especially in predicting draws and prediction during final moments of games. IGSOP provides a novel perspective to solve the problem of in-game outcome prediction in soccer, which has a potential ripple effect on related research.https://www.mdpi.com/1099-4300/24/7/971soccerprobability predictionin-game outcome predictionmachine learningBernoulli distributionregression coefficients |
| spellingShingle | Wendi Yao Yifan Wang Mengyao Zhu Yixin Cao Dan Zeng Goal or Miss? A Bernoulli Distribution for In-Game Outcome Prediction in Soccer Entropy soccer probability prediction in-game outcome prediction machine learning Bernoulli distribution regression coefficients |
| title | Goal or Miss? A Bernoulli Distribution for In-Game Outcome Prediction in Soccer |
| title_full | Goal or Miss? A Bernoulli Distribution for In-Game Outcome Prediction in Soccer |
| title_fullStr | Goal or Miss? A Bernoulli Distribution for In-Game Outcome Prediction in Soccer |
| title_full_unstemmed | Goal or Miss? A Bernoulli Distribution for In-Game Outcome Prediction in Soccer |
| title_short | Goal or Miss? A Bernoulli Distribution for In-Game Outcome Prediction in Soccer |
| title_sort | goal or miss a bernoulli distribution for in game outcome prediction in soccer |
| topic | soccer probability prediction in-game outcome prediction machine learning Bernoulli distribution regression coefficients |
| url | https://www.mdpi.com/1099-4300/24/7/971 |
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