People are talking a lot about the betting side, and those comments are right and applicable. But I want to mention the statistical side. This relates mainly to football, though there are nuances to it that translate to those sports as well.
Take last week’s AFC Championship game as a good example. Patrick Mahomes has played in a grand total of 97 career regular season games and 16 career postseason games (before that game). Lamar Jackson is similar at 86 career regular season games and for him just 5 career postseason games (before that game).
For both, that is a decent amount of starts in NFL terms. In statistical terms though, it’s really hard to determine meaningful trends versus statistical outliers. Especially when you factor in things like the rainy weather for that game. Now we are talking about just a couple dozen games each, and it becomes even harder to try to spot trends for each offense. As we have seen throughout the season, other players on the roster can greatly impact how well Mahomes and Jackson play. Mahomes’s receivers had a much higher drop rate this year than previous years. In a rainy game, you might expect that to continue, but it didn’t really on Sunday.
Even with all the amounts of data out there, there isn’t really enough to draw meaningful statistical models for accurate game by game predictions especially with how that translates to the final score to be useful in betting.
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