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

Beating the Spread: Testing the Efficiency of the Gambling Market for National Football League Games

1985 ◽  
Vol 93 (4) ◽  
pp. 800-806 ◽  
Author(s):  
Richard A. Zuber ◽  
John M. Gandar ◽  
Benny D. Bowers
Keyword(s):  
National Football League ◽  
Gambling Market

NFL betting market efficiency, divisional rivals, and profitable strategies

2019 ◽  
Vol 36 (4) ◽  
pp. 567-580
Author(s):  
Corey A. Shank
Keyword(s):  
Design Methodology ◽  
National Football League ◽  
Content Type ◽  
Home Team ◽  
Market Inefficiencies ◽  
Point Spread ◽  
Betting Market ◽  
Out Of Sample ◽  
Gambling Market ◽  
The Impact

Purpose This paper aims to examine market inefficiencies in the National Football League (NFL) betting market from the 2003 season to the 2016 season. Design/methodology/approach The author examines the impact that division rivals and previously known determinants of inefficiencies have on the current NFL gambling market. Findings The results show that games against division rivals have a lower chance of the home team covering the spread and the chance the game will result in an over. This result demonstrates that the sportsbooks underestimate the familiarity that teams have with each other’s players, coaches and tendencies from playing each other twice per year. Moreover, using this result in conjunction with previous known inefficiencies, the author puts forth a model to test out of sample predictions. The results from these tests show profitable strategies in the point spread and totals market with a win rate of nearly 57 per cent. Originality/value Overall, this paper demonstrates inefficiencies in the NFL betting market that future bettors may be able to take advantage of.

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.

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