It doesn't matter how long it took to find the stats... the point is, there is an argument going on about whether stats are always useful or not, and he just proved that they are not because they lie.
How did he prove stats lie?
I really don't get this.
I know one person who got in a car crash and would have died if they hadn't been thrown through the windshield. There, I proved with stats that seatbelts are useless.
There's a huge difference between NUMBERS and STATS. The previous poster threw out some NUMBERS that are commonly considered "good" or "bad." That's NOT statistics.
Statistics is tying independently observable measurements to specific outcomes.
For example, if you could show that, over the last twenty years, the third highest scoring rookie, INDEPENDENT of all other variables (shooting percentage, etc.), ended up being a good player (by some other measure), then yes, THAT would be evidence that Morrison should have been expected to become a good player. However, I don't think you will find that; I believe you will find there are OTHER indicators that more strongly correlate that have a history of usage and repeatable outcomes/repeated history of reasonable predictive value.
Same for turnover differential. If you compared turnover differential to win-loss record over the years, and found that it alone AND MORE STRONGLY THAN ANY OTHER MEASURE, tightly correlated with win-loss record, then you could reasonably conclude that Minnesota had an "unlucky" year, and was actually a good team, and would have a good record next year.
But that's not the case either. There is demonstrable history that the best predictor of a team's future record is their point differential. How teams GET to that point differential is quite varied and random: the C's have lots of turnovers and very good defense, etc.
Without the body of evidence and the history of proof correlation, numbers are NOT stats, they're just numbers. But just because random NUMBERS don't tell you much about what's going on (which seems obvious to me), doesn't mean there's not a huge value for statistical analysis.
TP for summing up a lot of what I was going to say. I don't want to speak for anyone specifically but there seems to be an attitude, in and out of this thread, that "If one statistic is misleading, this proves that all statistics are misleading!" Ironically this belief displays a lack of statistical understanding, and often gets used to justify continuing to be that way.
Minny being 3rd in the league at turnover differential is only a "lie" if you think the stat argues that good turnover differential can only happen for good teams. Of course the stat doesn't state or imply any such thing. It just means an otherwise bad team seems to be good at one aspect of the game. You have to misunderstand or misinterpret what the statistic means to claim it's "deceptive" in some way.
People, on the other hand, will often wield statistics in a misleading way, but that's a different story, and yet another reason why an independent understanding is useful.
All stats are midleading... well maybe not the stats, but the person reporting the stats has the ability to make them say whatever they want to say. In that way stats are always misleading. I don't know what else to say... you have a mathmetician on here saying that stats lie... I mentioned my statistics professor who said stats lie, I've given a very valid argument... and no I don't have an attitude and yet you say I have an attitude and continue to downplay my reasons for saying stats lie. You simply can not believe all stats, that's all there is to it.
Nothing personal, but this is kind of a throw the baby out with the bathwater approach. Stats can be misinterpreted or misleading, but that doesn't mean that they can't be used properly or that they can't paint an accurate picture.
TP.
Stats are very useful, but they have to be used wisely.
The alternative is complete subjectivity where people just say whatever they want with no responsibility to connect it with empirical reality.
Stats are particularly useful due to our many cognitive biases. We all suffer from confirmation bias where we tend to notice events that confirm what we fell and ignore events that disconfirm what we believe. The best example of this is the reality that for most teams, the average fan feels that their team, on average, has more bad calls go against them than go for them. This is why we can regularly see fans of both teams complaining the refs were against their respective teams in the same game.
There are also people who stat mine, seeking any stats that would seem to support their view.
A better use is to have a hypothesis, determine what stats would confirm that hypothesis BEFORE looking at the actual stats, and then look at the stats to see if they confirm the view.
Topic: Fouls drawn per field goal attempts for selected players (Read 18685 times)