On the Demand for Live Sport Contests: Insights from the Secondary Ticket Market for National Football League Games

2014 ◽  
Author(s):  
Mark Diehl ◽  
Joel G. Maxcy ◽  
Joris Drayer
Keyword(s):  
National Football League

Hold Your Bets: Another Look at the Efficiency of the Gambling Market for National Football League Games

1988 ◽  
Vol 96 (1) ◽  
pp. 206-213 ◽  
Author(s):  
Raymond D. Sauer ◽  
Vic Brajer ◽  
Stephen P. Ferris ◽  
M. Wayne Marr
Keyword(s):  
National Football League ◽  
Gambling Market

scholarly journals Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

2021 ◽  
Vol 11 (14) ◽  
pp. 6594
Author(s):  
Yu-Chia Hsu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Logistic Regression Model ◽  
National Football League ◽  
Performance Metrics ◽  
Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

scholarly journals Concussion in National Football League Athletes Is Not Associated With Increased Risk of Acute, Noncontact Lower Extremity Musculoskeletal Injury

2021 ◽  
Vol 9 (5) ◽  
pp. 232596712110034
Author(s):  
Toufic R. Jildeh ◽  
Fabien Meta ◽  
Jacob Young ◽  
Brendan Page ◽  
Kelechi R. Okoroha
Keyword(s):  
Lower Extremity ◽  
National Football League ◽  
Field Performance ◽  
Return To Play ◽  
Lower Extremity Injury ◽  
Extremity Injury ◽  
Lower Extremity Injuries ◽  
Increased Risk ◽  
Extremity Injuries ◽  
And Control

Background: Impaired neuromuscular function after concussion has recently been linked to increased risk of lower extremity injuries in athletes. Purpose: To determine if National Football League (NFL) athletes have an increased risk of sustaining an acute, noncontact lower extremity injury in the 90-day period after return to play (RTP) and whether on-field performance differs pre- and postconcussion. Study Design: Cohort study, Level of evidence, 3. Methods: NFL concussions in offensive players from the 2012-2013 to the 2016-2017 seasons were studied. Age, position, injury location/type, RTP, and athlete factors were noted. A 90-day RTP postconcussive period was analyzed for lower extremity injuries. Concussion and injury data were obtained from publicly available sources. Nonconcussed, offensive skill position NFL athletes from the same period were used as a control cohort, with the 2014 season as the reference season. Power rating performance metrics were calculated for ±1, ±2, and ±3 seasons pre- and postconcussion. Conditional logistic regression was used to determine associations between concussion and lower extremity injury as well as the relationship of concussions to on-field performance. Results: In total, 116 concussions were recorded in 108 NFL athletes during the study period. There was no statistically significant difference in the incidence of an acute, noncontact lower extremity injury between concussed and control athletes (8.5% vs 12.8%; P = .143), which correlates with an odds ratio of 0.573 (95% CI, 0.270-1.217). Days (66.4 ± 81.9 days vs 45.1 ± 69.2 days; P = .423) and games missed (3.67 ± 3.0 vs 2.9 ± 2.7 games; P = .470) were similar in concussed athletes and control athletes after a lower extremity injury. No significant changes in power ratings were noted in concussed athletes in the acute period (±1 season to injury) when comparing pre- and postconcussion. Conclusion: Concussed, NFL offensive athletes did not demonstrate increased odds of acute, noncontact, lower extremity injury in a 90-day RTP period when compared with nonconcussed controls. Immediate on-field performance of skill position players did not appear to be affected by concussion.

Prediction of sports attendance: A comparative analysis

2020 ◽  
pp. 175433712098313
Author(s):  
Mehmet Şahin ◽  
Murat Uçar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Comparative Analysis ◽  
National Football League ◽  
Relevant Literature ◽  
Performance Evaluations ◽  
Influential Factor ◽  
Gradient Boosting ◽  
Home Team ◽  
Machine Learning Methods

In this study, a comparative analysis for predicting sports attendance demand is presented based on econometric, artificial intelligence, and machine learning methodologies. Data from more than 20,000 games from three major leagues, namely the National Basketball Association (NBA), National Football League (NFL), and Major League Baseball (MLB), were used for training and testing the approaches. The relevant literature was examined to determine the most useful variables as potential regressors in forecasting. To reveal the most effective approach, three scenarios containing seven cases were constructed. In the first scenario, each league was evaluated separately. In the second scenario, the three possible combinations of league pairings were evaluated, while in the third scenario, all three leagues were evaluated together. The performance evaluations of the results suggest that one of the machine learning methods, Gradient Boosting, outperformed the other methods used. However, the Artificial Neural Network, deep Convolutional Neural Network, and Decision Trees also provided productive and competitive predictions for sports games. Based on the results, the predictions for the NBA and NFL leagues are more satisfactory than the predictions of the MLB, which may be caused by the structure of the MLB. The results of the sensitivity analysis indicate that the performance of the home team is the most influential factor for all three leagues.

scholarly journals Jones Fractures Identified at the National Football League Scouting Combine: Assessment of Prognostic Factors, Computed Tomography Findings, and Initial Career Performance

2018 ◽  
Vol 6 (8) ◽  
pp. 232596711879074 ◽  
Author(s):  
Robert C. Spang ◽  
Daniel B. Haber ◽  
Brendin R. Beaulieu-Jones ◽  
Kristen L. Stupay ◽  
George Sanchez ◽  
...  
Keyword(s):  
Computed Tomography ◽  
National Football League ◽  
Treatment Method ◽  
Level Of Evidence ◽  
Significant Difference ◽  
Specific Performance ◽  
Incomplete Healing ◽  
Bony Healing ◽  
The Mean ◽  
Career Performance

Background: Jones fractures result in subsequent dysfunction and remain an issue for athletes. Purpose: To (1) describe the epidemiology, treatment, and impact of Jones fractures identified at the National Football League (NFL) Scouting Combine on players’ early careers and (2) establish the value of computed tomography (CT) to determine bony healing after a fracture in prospective players. Study Design: Cohort study; Level of evidence, 3. Methods: All players who attended the combine between 2009 and 2015 were retrospectively reviewed to identify their history of Jones fractures. The playing position, treatment method, and number of missed collegiate games were recorded. The mean overall draft pick number, number of games started and played, snap percentage, and position-specific performance scores (fantasy score) over the first 2 years in the NFL were compared between players with fractures and controls. An imaging classification system was applied based on grading of each quadrant of the fifth metatarsal (plantar, dorsal, medial, lateral), with a score of 0 for not healed or 1 for healed. Results: Overall, the number of Jones fractures identified was 72 in 2285 athletes (3.2%), with all treated via intramedullary screw fixation. The mean overall draft pick number for players with fractures was 111.2 ± 67.9 compared with 99.0 ± 65.9 for controls ( P = .12). Performance scores for players with fractures were lower than those for controls across all positions, with a significant difference in running backs (2.6 vs 4.0, respectively; P < .001) and defensive linemen (1.4 vs 2.3, respectively; P = .02). The mean CT score was 2.5 ± 1.3. Of the 32 athletes who underwent imaging, 16 Jones fractures (50.0%) were healed or nearly healed, 12 (37.5%) were partially healed, and 4 (12.5%) showed little or no healing. The plantar cortex demonstrated the least healing (18/32; 56.3%), followed by the lateral cortex (15/32; 46.9%). Players with a mean score <1 were found to have fewer games started (2.7 ± 2.5) than those with 1 to 3 cortices healed (17.4 ± 10.4) or all cortices healed (8.7 ± 11.2). Conclusion: Based on CT, 50% of all players with a previous Jones fracture demonstrated incomplete healing. Moreover, position-specific performance scores over the first 2 years of a player’s career were lower across all positions for those with fractures compared with controls. Players with CT scores <1 were found to start fewer games and were drafted later than controls.

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