Editing Prediction (section)

==Sports==

Predicting the outcome of sporting events is a business which has grown in popularity in recent years.  Handicappers predict the outcome of games using a variety of mathematical formulas, simulation models or [[Qualitative research|qualitative analysis]].  Early, well known sports bettors, such as [[Jimmy the Greek]], were believed to have access to information that gave them an edge.  Information ranged from personal issues, such as gambling or drinking to undisclosed injuries; anything that may affect the performance of a player on the field.

Recent times have changed the way sports are predicted.  Predictions now typically consist of two distinct approaches: Situational plays and statistical based models.  Situational plays are much more difficult to measure because they usually involve the motivation of a team.  Dan Gordon, noted handicapper, wrote "Without an emotional edge in a game in addition to value in a line, I won't put my money on it".<ref>{{cite book | author = Gordon, Dan
 | date = 2005
 | title = Beat the Sports Books
 | publisher = Cardoza
 | location = New York, United States
 | isbn = 978-1-5804-2174-4
 | url = http://cardozabooks.com/BEAT-THE-SPORTS-BOOKS.html}}</ref>  These types of plays consist of: Betting on the home underdog, betting against Monday Night winners if they are a favorite next week, betting the underdog in "look ahead" games etc.  As situational plays become more widely known they become less useful because they will impact the way the line is set.

The widespread use of technology has brought with it more modern [[sports betting systems]].  These systems are typically algorithms and simulation models based on [[regression analysis]]. [[Jeff Sagarin]], a sports statistician, has brought attention to sports by having the results of his models published in USA Today.  He is currently paid as a consultant by the [[Dallas Mavericks]] for his advice on lineups and the use of his Winval system, which evaluates free agents. [[Advanced NFL Stats|Brian Burke]], a former [[United States Navy|Navy]] fighter pilot turned sports statistician, has published his results of using regression analysis to predict the outcome of NFL games.<ref>{{cite web | author = Burke, Brian
 | date = 2008
 | title = NFL Win Prediction Methodology
 | url = http://www.advancednflstats.com/2007/09/nfl-win-prediction-methodology.html}}</ref> [[Ken Pomeroy]] is widely accepted as a leading authority on college basketball statistics.  His website includes his College Basketball Ratings, a tempo based statistics system.  Some statisticians have become very famous for having successful prediction systems.  Dare wrote "the effective odds for sports betting and horse racing are a direct result of human decisions and can therefore potentially exhibit consistent error".<ref>{{cite book
 | author = Dare, William H
 | date = 2006
 | title = Risk Return and Gambling Market Efficiency
 | publisher = Oklahoma State University
 | location = Oklahoma City, United States
 | url = http://finance.ba.ttu.edu/new/documents/researchSeminars/fall2006/Risk%20and%20Gambling%20Market%20Efficiency.pdf
 | archive-date = 2013-07-19
 | access-date = 2012-11-05
 | archive-url = https://web.archive.org/web/20130719203131/http://finance.ba.ttu.edu/new/documents/researchSeminars/fall2006/Risk%20and%20Gambling%20Market%20Efficiency.pdf
 | url-status = dead
 }}</ref>  Unlike other games offered in a casino, prediction in sporting events can be both logical and consistent.

Other more advance models include those based on Bayesian networks, which are causal probabilistic models commonly used for risk analysis and decision support. Based on this kind of mathematical modelling, Constantinou et al.,<ref>{{cite journal|last=Constantinou|first=Anthony|author2=Fenton, N. |author3=Neil, M. |title=pi-football: A Bayesian network model for forecasting Association Football match outcomes|journal=Knowledge-Based Systems|year=2012|volume=36|pages=322–339|doi=10.1016/j.knosys.2012.07.008|citeseerx=10.1.1.420.4110|url=http://www.eecs.qmul.ac.uk/~norman/papers/pi-football%20%20A%20Bayesian%20network%20model%20for%20forecasting%20Association%20Football%20match%20outcomes.pdf}}</ref><ref>{{cite journal|last=Constantinou|first=Anthony|author2=Fenton, N. |author3=Neil, M. |title=Profiting from an inefficient Association Football gambling market: Prediction, Risk and Uncertainty using Bayesian networks.|journal=Knowledge-Based Systems|year=2013|volume=50|pages=60–86|doi=10.1016/j.knosys.2013.05.008|doi-access=free}}</ref> have developed models for predicting the outcome of association football matches.<ref>{{cite web|url=https://lootrs.com/football-predictions/|title=Football prediction|publisher=Lootrs|access-date =24 Jan 2023|first=Mahesh|last=Patel|date=24 Jan 2023}}</ref> What makes these models interesting is that, apart from taking into consideration relevant historical data, they also incorporate all these vague subjective factors, like availability of key players, team fatigue, team motivation and so on. They provide the user with the ability to include their best guesses about things that there are no hard facts available. This additional information is then combined with historical facts to provide a revised prediction for future match outcomes. The initial results based on these modelling practices are encouraging since they have demonstrated consistent profitability against published market odds.

Nowadays sport betting is a huge business; there are many websites (systems) alongside betting sites, which give tips or predictions for future games.<ref>{{cite web|title=Soccer picks and predictions|url=http://soccerpunt.com/|website=Soccer Punt}}</ref> Some of these prediction websites (tipsters) are based on human predictions, but others on computer software sometimes called prediction robots or bots. Prediction bots can use different amount of data and algorithms and because of that their accuracy may vary.

These days, with the development of artificial intelligence, it has become possible to create more consistent predictions using statistics. Especially in the field of sports competitions, the impact of artificial intelligence has created a noticeable consistency rate. On the science of [https://soccerseer.com AI soccer predictions], an initiative called soccerseer.com, one of the most successful systems in this sense, manages to predict the results of football competitions with up to 75% accuracy with artificial intelligence.