I'll throw out there this additional explanation for anyone still wondering how they get to 9 losses (1 loss we already have + 8 more they are forecasting going forward), which might be helpful to anyone reading this who hasn't seen the statistical concept of Expected Value before. Not everyone has but it's pretty useful, so it may be worthwhile to explain. It's not complicated really, so I think most people do or can understand it pretty readily after it's been explained. If anybody wants more info about it or has questions you can post or PM me.
To make an estimate of the overall Expected Value for a forecast of the outcome of a group of events, like to estimate the number of wins and losses in the group of remaining games in a basketball season, you simply add together your estimate of the probabilities of the outcome for each event.
For example, lets say there were only 5 games remaining and this was your estimate of the probability of winning each of those 5 games
Game Prob of Winning
1 90%
2 70%
3 50%
4 50%
5
30%
Total 290%
To convert that total of the probabilities of 290% into number of wins, you just divide by 100%. So 290% divided by 100% = 2.9 wins. So the Expected Value of the group of % forecasts you made for those 5 games is 2.9 wins. Of course you can't win or lose fractions of games, so they will generally round up and predict to win 3 of the games, and lose 2.
That's how they got to losing 8 more games going forward, simply by adding up all those individual game probability predictions. It gave a record of 16-8 for the remaining 24 games. *
The basis for this approach is simply that overall you are likely to win half the games you think you have a 50% chance of winning, win a few you think you have little chance of winning going in, and lose a few you think you have a great chance of winning going in. Overall, if your % forecasts were pretty good going in, you should end up with about the number of wins and losses that calculating the Expected Value predicted.
* If you want to eyeball check it, you can do it in about a minute, simply by taking all those winning %s they estimated, rounding them to the nearest 10%, and then adding them. That would give you only 13.6 wins, not the 16 wins they predict for the remaining 24 games. But you'll notice the list of least and most likely wins only have 20 games total between them. They left 4 games out, probably because these are lists of the 10 hardest and 10 easiest games, a nice round number.
But when you do an Expected Value calculation like this you must have a value for each event in order to be accurate. Since the remaining 4 games are not on either the most or least likely win lists, it's reasonable to assume the likelihood of each of those games is a number between the two lists of predictions. The lowest winning % on the list of easiest games is 76%; the highest winning % on the list of hardest games is 68%. So it would be reasonable to assume the forecast for the 4 games they left out was between 68% and 76%, or, say, about 70%. The Expected Value of wins from those 4 additional games would be 70% times 4 games, or 280%, or 2.8 wins. So you would add those to the total you eyeball added for the two lists, giving a total of 16.4, which rounds down to 16 wins, or a forecast of 16-8.
(Note: Basil liked this post when I had temporarily edited it to say "nm" after I first posted it, but then decided to take it down and fix it. So now that I put it back up, he has the right to say he hates it. I'd be surprised if he or anyone even reads it, so I'm fine either way.
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