The obvious question is …. what are “sports data models” ???
Basically …. they are machine learning models created specifically to forecast the outcomes of sporting events.
OK … the next question is … what does that mean ???
Machine learning (or artificial intelligence) models are simply mathematical (or statistical) processes that analyze past data, identify predictive attributes, then re-apply the learned “coefficients” to predict future events. (Actually, this is somewhat of a simplification … there are other types, but our models are “predictive” rather than cluster, classification or time-series models.)
SDM models include …
our equine model looked at a large number of past races and evaluated which attributes best predict how fast a horse may run in the next race …
our NFL model looked at individual games over a number of years to understand which offensive and defensive metrics can predict how many points a team will score and allow against the next opponent …
our NCAA basketball model not only evaluates offensive and defensive game metrics (points, turn-overs, rebounds, time of possession, 2 & 3-point-efficiency) but also has to create intra-conference coefficients to adjust for the opponent play in different conferences …
our NCAA football model incorporates offensive and defensive metrics (passing yards, rushing yards, turn-overs, points scored/allowed, plays/game, opponents schedule)
Finally … there is no “Magic Box” that will accurately forecast EVERY event. A good model will predict the majority of outcomes over an extended period … but there is NO sure thing in any sporting event.
THE KEY OBJECTIVE … look at the model forecast, select your pick and decide … IS THIS A PROFITABLE BET?
