Open Access

A Team-Compatibility Decision Support System for the National Football League


Cite

Abrams, W., Barnes, J. C., & Clement, A. (2008). Relationship of Selected pre-NBA Career Variables to NBA Players’ Career Longevity. The Sport Journal, 11(2).Search in Google Scholar

Al-Shboul, R., Syed, T., Memon, J., & Khan, F. (2017). Automated Player Selection for a Sports Team using Competitive Neural Networks. International Journal of Advanced Computer Science and Applications, 8(8), 1-4.10.14569/IJACSA.2017.080859Search in Google Scholar

Barron, D., Ball, G., Robins, M., & Sunderland, C. (2018). Artificial Neural Networks and Player Recruitment in Professional Soccer. PLoS ONE, 13(10). doi:https://doi.org/10.1371/journal.pone.020581810.1371/journal.pone.0205818620922530379858Search in Google Scholar

Beyer, K., Fukuda, D., Redd, M., Stout, J., & Hoffman, J. (2016). Player Selection Bias in National Football League Draftees. The Journal of Strength & Conditioning Research, 30(11), 2965-2971.10.1519/JSC.000000000000142627780175Search in Google Scholar

Birnbaum, P. (2019). A Guide To Sabermetric Research. Retrieved February 11, 2019, from https://sabr.org/sabermetricsSearch in Google Scholar

CBS Sports. (2016). 19 of the biggest NFL Draft busts ever. Retrieved from http://www.cbssports.com/nfl/photos/biggest-draft-busts-everSearch in Google Scholar

Chien, C., & Chen, L. (2007). Using Rough Set Theory to Recruit and Retain High-Potential Talents for Semiconductor Manufacturing. IEEE Transactions on Semiconductor Manufacturing, 20(4), 528-541.10.1109/TSM.2007.907630Search in Google Scholar

Chien, C., & Chen, L. (2008). Data Mining to Improve Personnel Selection and Enhance Human Capital: A Case Study in the High-technology Industry. Expert Systems with Applications, 34, 280-290.10.1016/j.eswa.2006.09.003Search in Google Scholar

Clark, T., & Jones, M. (2007). Achieving Competitive Advantage in a High Turnover, Dynamic Market. International Conference of the System Dynamics Society, (pp. 1-15). Boston, Massachusetts, USA.Search in Google Scholar

Clason, D., & Dormody, T. (1994). Analyzing Data Measured by Individual Likert-Type Items. Journal of Agricultural Education, 35(4), 31-35.10.5032/jae.1994.04031Search in Google Scholar

Clemente, F., & Martins, F. (2017). Network Structure of UEFA Champions League Teams: Association with Classical Notational Variables and Variance Between Different Levels of Success. International Journal of Computer Science in Sport, 16(1), 39-50.10.1515/ijcss-2017-0003Search in Google Scholar

Coates, D. (2002). The Economic Impact of Postseason Play in Professional Sports. Journal of Sports Economics, 3(3), 291-299.10.1177/1527002502003003005Search in Google Scholar

Demir, E. (2014). A Decision Support Tool for Predicting Patients at Risk of Readmission: A Comparison of Classification Trees, Logistic Regression, Generalized Additive Models, and Multivariate Adaptive Regression Splines. Decision Sciences, 45(5), 849-880.10.1111/deci.12094Search in Google Scholar

Deshpande, S., & Jenson, S. (2016). Estimating an NBA player’s Impact on His Team’s Chances of Winning. Journal of Quantitative Analysis in Sports, 12, 51-72.10.1515/jqas-2015-0027Search in Google Scholar

Ebben, W., & Blackard, D. (2001). Strength and Conditioning Practices of National Football League Strength and Conditioning Coaches. Journal of Strength and Conditioning Research, 15(1), 48–58.Search in Google Scholar

ESPN. (2004, Apr. 25). NFL Draft-pick Value Chart. Retrieved Dec 28, 2009, from ESPN Insider: http://sports.espn.go.com/espn/print?id=2410670&type=storySearch in Google Scholar

Football’s Future. (2007). 2007 NFL Free Agents. Retrieved Mar. 13, 2009, from http://www.footballsfuture.com/2007/nfl/freeagents.htmlSearch in Google Scholar

Forbes. (2018). NFL Team Valuations. Retrieved from https://www.forbes.com/nfl-valuations/list/#tab:overallSearch in Google Scholar

Fry, M., Lundberg, A., & Ohlmann, J. (2007). A Player Selection Heuristic for a Sports League Draft. Journal of Quantitative Analysis in Sports, 3(2), 1-35.10.2202/1559-0410.1050Search in Google Scholar

Gavião, L., Sant’Anna, A., Lima, G., & Garcia, P. (2019). Evaluation of Soccer Players Under the Moneyball Concept. Journal of Sports Sciences, 1-21.Search in Google Scholar

Goldstein, G., & Hersen, M. (1984). Handbook of Psychological Assessment. New York: Pergamon Press.Search in Google Scholar

Hartman, M. (2011). Competitive Performance Compared to Combine Performance as a Valid Predictor of NFL Draft Status. Journal of Strength & Conditioning Research, 25.10.1097/01.JSC.0000395746.03546.e8Search in Google Scholar

Hendricks, W., DeBrock, L., & Koenker, R. (2003). Uncertainty, Hiring, and Subsequent Performance: The NFL Draft. Journal of Labor Economics, 21(4), 857-886.10.1086/377025Search in Google Scholar

Hilbert, D. (1902). Mathematical Problems. Bulletin of the American Mathematical Society, 8, 437-479.10.1090/S0002-9904-1902-00923-3Search in Google Scholar

Hsu, P., Galsanbadam, S., Yang, Y., & Yang, C. (2018). Evaluating Machine Learning Varieties for NBA Players’ Winning Contribution. International Conference on System Science and Engineering (ICSSE), (pp. 1-6). New Taipei.10.1109/ICSSE.2018.8520017Search in Google Scholar

Hvattum, L. M. (2019). A Comprehensive Review of Plus-Minus Ratings for Evaluating Individual Players in Team Sports. International Journal of Computer Science in Sport, 18(1), 1-23.10.2478/ijcss-2019-0001Search in Google Scholar

James, B. (1979). The Bill James Abstract. self-published.Search in Google Scholar

James, B. (1985). The Bill James Historical Baseball Abstract. Villard.Search in Google Scholar

Jamieson, S. (2004). Likert Scales: How to (Ab)use Them. Medical Education, 38, 1212-1218.10.1111/j.1365-2929.2004.02012.x15566531Search in Google Scholar

Larsen, A., & Fenn, A. (2006). The Impact of Free Agency and the Salary Cap on Competitive Balance in the National Football League. Journal of Sports Economics, 7(4), 374-390.10.1177/1527002505279345Search in Google Scholar

Lewin, D. (2007). 2007 Quarterbacks Draft Preview. Retrieved from Football Outsiders: http://www.footballoutsiders.com/nfl-draft/2007/2007-quarterbacks-draft-previewSearch in Google Scholar

Lubke, G., & Muthen, B. (2004). Applying Multigroup Confirmatory Factor Models for Continuous Outcomes to Likert Scale Data Complicates Meaningful Group Comparisons. Structural Equation Modeling, 514-534, 11.10.1207/s15328007sem1104_2Search in Google Scholar

Macey, N. (2005, Jan. 19). How Much Should Rookie Quarterbacks Play? Retrieved Nov. 20, 2009, from Football Outsiders: http://www.footballoutsiders.com/stat-analysis/2005/how-much-should-rookie-quarterbacks-playSearch in Google Scholar

Massey, C., & Thaler, R. (2005). Overconfidence vs. Market Efficiency in the National Football League. Working Paper W11270, National Bureau of Economic Research (NBER).Search in Google Scholar

Mirabile, M. (2007). The NFL Rookie Cap: An Empirical Analysis of One of the NFL’s Most Closely Guarded Secrets. The Sport Journal, 10(3), 1-8.Search in Google Scholar

Moy, D. (2006). Regression Planes to Improve the Pythagorean Percentage: A regression model using common baseball statistics to project offensive and defensive efficiency. MS Thesis, University of California Berkeley, Statistics, Berkeley, CA.Search in Google Scholar

Mulholland, J., & Jensen, S. T. (2014). Predicting the Draft and Career Success of Tight Ends in the National Football League. Journal of Quantitative Analysis in Sports, 10, 381-396.10.1515/jqas-2013-0134Search in Google Scholar

Nagarajan, R., & Li, L. (2017). Optimizing NBA Player Selection Strategies Based on Salary and Statistics Analysis. IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), (pp. 1076-1083). Orlando, FL.10.1109/DASC-PICom-DataCom-CyberSciTec.2017.175Search in Google Scholar

Newman, D., & Sin, H. (2009). How do Missing Data Bias Estimates of Within-group Agreements? Sensitivity of SDwg, CVwg, rwg(j), rWG(j)*, and ICC to Systematic Nonresponse. Organizational Research Methods, 12(1), 113-147.10.1177/1094428106298969Search in Google Scholar

Ng, F., Rouse, P., & Harrison, J. (2016). Classifying Revenue Management: A Taxonomy to Assess Business Practice. Decision Sciences, 1-34.Search in Google Scholar

Oztekin, A., & Riaz Khan, M. (2014). A Business-Analytic Approach to Identify Critical Factors in Quantitative Disciplines. Journal of Computer Information Systems, 54, 60-70.10.1080/08874417.2014.11645723Search in Google Scholar

Oztekin, A., Delen, D., & Turkyilmaz, A. Z. (2013). A Machine Learning-based Usability Evaluation Method for eLearning Systems. Decision Support Systems, 56, 63-73.10.1016/j.dss.2013.05.003Search in Google Scholar

Oztekin, A., Kizilaslan, R., Freund, S., & Iseri, A. (2016). A Data Analytic Approach to Forecasting Daily Stock Returns in an Emerging Market an Emerging Market. European Journal of Operational Research,, 253(3), 697-710.10.1016/j.ejor.2016.02.056Search in Google Scholar

Pantuso, G. (2017). The Football Team Composition Problem: A Stocastic Programming Approach. Journal of Quantitative Analysis in Sports, 13, 113-129.10.1515/jqas-2017-0030Search in Google Scholar

Perl, J., & Memmert, D. (2017). A Pilot Study on Offensive Success in Soccer Based on Space and Ball Control – Key Performance Indicators and Key to Understand Game Dynamics. International Journal of Computer Science in Sport, 16(1), 65-75.10.1515/ijcss-2017-0005Search in Google Scholar

Peterson, K., & Evans, L. (2019). Decision Support System for Mitigating Athletic Injuries. International Journal of Computer Science in Sport, 18(1), 45-63.10.2478/ijcss-2019-0003Search in Google Scholar

Pitts, J. D., & Evans, B. (2018). Evidence on the Importance of Cognitive Ability Tests for NFL Quarterbacks: What are the Relationships Among Wonderlic Scores, Draft Position and NFL Performance Outcomes? Applied Economics, 50, 2957-2966.10.1080/00036846.2017.1412081Search in Google Scholar

Pitts, J., & Evans, B. (2018). Drafting for Success: How Good Are NFL Teams at Identifying Future Productivity at Offensive-Skill Positions in the Draft? The American Economist, 64(1), 102-122.10.1177/0569434518812678Search in Google Scholar

Pro-Football Reference. (2009). Retrieved from Coaches, Records, and Coaching Totals: http://www.pro-football-reference.com/coaches/Search in Google Scholar

Robbins, D. (2011). Positional Physical Characteristics of Players Drafted Into the National Football League. J Strength Cond Res, 25(10), 2661-2667.10.1519/JSC.0b013e318208ae3f21886010Search in Google Scholar

Rothstein, M., & Goffin, R. (2006). The Use of Personality Measures in Personnel Selection: What does current research support? Human Resource Management Review, 15, 155-180.10.1016/j.hrmr.2006.03.004Search in Google Scholar

Saikia, H., Bhattacharjee, D., & Radhkrishnan, U. (2016). A New Model for Player Selection in Cricket. International Journal of Performance Analysis in Sport, 16(1), 373-388.10.1080/24748668.2016.11868893Search in Google Scholar

Schatz, A. (2004). Method to Our Madness. Retrieved Nov. 20, 2009, from Football Outsiders: http://www.footballoutsiders.com/info/methodsSearch in Google Scholar

Schatz, A. (2005). Football’s Hilbert Problems. Journal of Quantitative Analysis in Sports, 1 (1), 1-8.10.2202/1559-0410.1010Search in Google Scholar

Scheffer, J. (2002). Dealing with Missing Data. Research Letters in the Information and Mathematical Sciences, 3, 153-160.Search in Google Scholar

Schuckers, M. (2011). An Alternative to the NFL Draft Pick Value Chart Based upon Player Performance. Journal of Quantitative Analysis in Sports, 7(2), 1-14.10.2202/1559-0410.1329Search in Google Scholar

Sevim, C., Oztekin, A., Bali, O., Gumus, S., & Guresen, E. (2014). Developing an Early Warning System to Predict Currency Crises. European Journal of Operational Research, 237(3), 1095-1104.10.1016/j.ejor.2014.02.047Search in Google Scholar

Shadabi, F., Sharma, D., & Cox, R. (2006). Learning from Ensembles: Using Artificial Neural Network Ensemble for Medical Outcomes Prediction. Innovations in Information Technology, 1-5.10.1109/INNOVATIONS.2006.301896Search in Google Scholar

Sisson, D., & Stocker, H. (1981). Analyzing and Interpreting Likert-type survey data. The Delta Pi Epsilon Journal, 31(2), 81-85.Search in Google Scholar

STATS Inc. (2007). Retrieved from http://www.stats.comSearch in Google Scholar

Stern, H. (1998). American Football. In J. Bennett, Statistics in Sport (pp. 3-23). London: Arnold Applications of Statistics.Search in Google Scholar

Strohmeier, S., & Piazza, F. (2013). Domain Driven Data Mining in Human Resource Management: A Review of Current Research. Expert Systems with Applications, 40, 2410-2420.10.1016/j.eswa.2012.10.059Search in Google Scholar

Thieme, J., Song, M., & Calantone, R. (2000). Artificial Neural Network Decision Support Systems for New Product Development Project Selection. Journal of Marketing Research, 37(4), 499-507.10.1509/jmkr.37.4.499.18790Search in Google Scholar

Uzochukwu, O., & Enyindah, P. (2015). A Machine Learning Application for Football Players’ Selection. International Journal of Engineering Research & Technology, 4(10), 459-465.Search in Google Scholar

Whiting, S., & Maynes, T. (2016). Selecting Team Players: Considering the Impact of Contextual Performance and Workplace Deviance on Selection Decisions in the National Football. Journal of Applied Psychology, 101(4), 484-497.10.1037/apl000006726595758Search in Google Scholar

Wolfson, J., Addona, V., & Schmicker, R. (2011). The Quarterback Prediction Problem: Forecasting the Performance of College Quarterbacks Selected in the NFL Draft. Journal of Quantitative Analysis in Sports, 7(3), 1-22.10.2202/1559-0410.1302Search in Google Scholar

Yaldo, L., & Shamir, L. (2017). Computational Estimation of Football Player Wages. International Journal of Computer Science in Sport, 16(1), 18-38.10.1515/ijcss-2017-0002Search in Google Scholar

Young, W., Weckman, G., & Holland, W. (2009). A Survey of Methodologies for the Treatment of Missing Values within Datasets: Limitations and Benefits. Theoretical Issues in Ergonomics Science.Search in Google Scholar

eISSN:
1684-4769
Language:
English
Publication timeframe:
2 times per year
Journal Subjects:
Computer Sciences, Databases and Data Mining, other, Sports and Recreation, Physical Education