Football Player Performance Prediction Based on Combined Kernel Function Correlation Vector Machine

2020 ◽  
Vol 29 (5) ◽  
Author(s):  
Tan Qingwen
Keyword(s):  
Kernel Function ◽  
Performance Prediction ◽  
Football Player ◽  
Correlation Vector ◽  
Function Correlation ◽  
Player Performance

Momentum, Rather Than Velocity, Is a More Effective Measure of Improvements in Division IA Football Player Performance

2022 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
J. Bryan Mann ◽  
Jerry L. Mayhew ◽  
Marcel Lopes Dos Santos ◽  
J. Jay Dawes ◽  
Joseph F. Signorile
Keyword(s):  
Football Player ◽  
Effective Measure ◽  
Player Performance

A comparative study of Gaussian process regression with other three machine learning approaches in the performance prediction of centrifugal pump

2021 ◽  
pp. 095440622110505
Author(s):  
Xutao Zhao ◽  
Desheng Zhang ◽  
Renhui Zhang ◽  
Bin Xu
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Centrifugal Pump ◽  
Kernel Function ◽  
Performance Prediction ◽  
Optimization Design ◽  
Gaussian Process Regression ◽  
Kernel Functions ◽  
Performance Indices ◽  
Pump Performance

Accurate prediction of performance indices using impeller parameters is of great importance for the initial and optimal design of centrifugal pump. In this study, a kernel-based non-parametric machine learning method named with Gaussian process regression (GPR) was proposed, with the purpose of predicting the performance of centrifugal pump with less effort based on available impeller parameters. Nine impeller parameters were defined as model inputs, and the pump performance indices, that is, the head and efficiency, were determined as model outputs. The applicability of three widely used nonlinear kernel functions of GPR including squared exponential (SE), rational quadratic (RQ) and Matern5/2 was investigated, and it was found by comparing with the experimental data that the SE kernel function is more suitable to capture the relationship between impeller parameters and performance indices because of the highest R square and the lowest values of max absolute relative error (MARE), mean absolute proportional error (MAPE), and root mean square error (RMSE). In addition, the results predicted by GPR with SE kernel function were compared with the results given by other three machine learning models. The comparison shows that the GPR with SE kernel function is more accurate and robust than other models in centrifugal pump performance prediction, and its prediction errors and uncertainties are both acceptable in terms of engineering applications. The GPR method is less costly in the performance prediction of centrifugal pump with sufficient accuracy, which can be further used to effectively assist the design and manufacture of centrifugal pump and to speed up the optimization design process of impeller coupled with stochastic optimization methods.

scholarly journals Outcomes of Concussion Injuries to Running Backs and Wide Receivers in the National Football League

2019 ◽  
Vol 7 (7_suppl5) ◽  
pp. 2325967119S0041
Author(s):  
Kelechi R. Okoroha ◽  
Toufic Raja Jildeh ◽  
Kevin Taylor ◽  
Patrick Buckley ◽  
Samir Mehta ◽  
...  
Keyword(s):  
National Football League ◽  
Football Player ◽  
Return To Play ◽  
Total Power ◽  
Control Group ◽  
Professional Football ◽  
Four Seasons ◽  
Before And After ◽  
Player Performance ◽  
The Impact

Objectives: Concussion injuries are common in professional football players, however the impact on player careers remains unclear. The purpose of this study was to quantify the effect of concussions on professional football player performance. Methods: Concussion data from the National Football League was collected for a period of four seasons (2012-2015) for running backs and wide receivers. Demographic variables (age, experience, position, time to return, yearly total yards and touchdowns) were recorded. Power ratings (total yards divided by 10 plus touchdowns multiplied by 6) were calculated for the injury season as well as for the 3 seasons before and after the injury. A control group consisted of running backs and wide receivers without an identified concussion injury who competed in the 2014 season. Results: One hundred and eighteen running backs and wide receivers sustained a concussion over a 4-season period, 25 players (21%) never returned to a National Football League game. Players were able to return in an average of 18.5 ± 8.2 days, missing 1.6 ± 1.0 games. For 18 players with a minimum total power rating of (sum of 4 seasons) of 200 points, power rating per game decreased 43.4 ± 0.4 points from three seasons prior to the concussion to three years postinjury. This change in performance was not statistically significant (P=0.422) when compared with the change for the 343 control players. Conclusion: Over one fifth of National Football League running backs and wide receivers who sustain a concussion never return to play in a game. On return to competition, player performance of injured players reduced from before injury, however there was no difference compared to controls.

scholarly journals Application of Artificial Intelligence in Basketball Sport

2021 ◽  
Vol 11 (7) ◽  
pp. 54-67 ◽  
Author(s):  
Bin Li ◽  
Xinyang Xu
Keyword(s):  
Artificial Intelligence ◽  
Performance Prediction ◽  
Sports Injuries ◽  
Sports Injury ◽  
Economic Benefits ◽  
Training Level ◽  
Application Research ◽  
Basketball Players ◽  
Research Investment ◽  
Player Performance

Basketball is among the most popular sports in the world, and its related industries have also produced huge economic benefits. In recent years, the application of artificial intelligence (AI) technology in basketball has attracted a large amount of attention. We conducted a comprehensive review of the application research of AI in basketball through literature retrieval. Current research focuses on the AI analysis of basketball team and player performance, prediction of competition results, analysis and prediction of shooting, AI coaching system, intelligent training machine and arena, and sports injury prevention. Most studies have shown that AI technology can improve the training level of basketball players, help coaches formulate suitable game strategies, prevent sports injuries, and improve the enjoyment of games. At the same time, it is also found that the number and level of published papers are relatively limited. We believe that the application of AI in basketball is still in its infancy. We call on relevant industries to increase their research investment in this area, and promote the improvement of the level of basketball, making the game increasingly exciting as its worldwide popularity continues to increase.

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