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As a quick evaluation of the accuracy of the model (at least in hindsight) we can look at the games played so far and see how many wins (and against which teams) the model predicted, versus how many the team actually has.
Then, based on a standard normal distribution, we can use the average and standard deviation to assign each game a win probability based on the opponent, location and back-to-back situation.
But, since the fun part (besides, you know, the basketball) is projecting forward and guessing at what this means for the season as a whole, let’s do that too.
Looking at that data set, the Raptors’ average performances come out as 10.3 on offense (insanely good) and -0.4 on defense (essentially average).
That context can make judging a team’s performance difficult to do, both over larger samples and over short stretches.
We’ll try to correct that difficulty in this piece (and the subsequent pieces to follow), by examining each game performance while taking into account how good the opponent is, where the team is playing, and schedule impacts.
And that’s how we’ll determine the quality of opponent — each team will be represented by their ORTG and DRTG for the year. Once the game is done, the Raptors will have an ORTG and DRTG for that individual game — the amount of points scored and allowed, adjusted for the total number of possessions played in the game.