3 ms·

>> small sample size Here is where a classically trained statistician can really shine. Classical statistics is really all about extracting insight from data w

• chollida1 11y ago
  I'm dubious of this claim. The average player's career is 3 years, the average coaches tenure is 3 years. Teams implement new offensive and defensive schemes even more frequently than that. Couple those facts with a short season( 16 games) and injuries limiting most players to less than that and it means you really can't use historical stats with any certainty. Essentially you start from scratch each season. I mean there is a reason why the phrase "statistically significant" is often used. if you can figure out how a "classically" trained statistician can create an acurate model with: 1) almost no data 2) inaccurate data 3) seasons in which the next one often bears no resemblance to the previous one ....then please feel free to contact me!
  • mailshanx 11y ago
    >> I mean there is a reason why the phrase "statistically significant" is often used. Not sure if this is meant to be satire / sarcasm? By "classically" trained statistician, i meant someone who has taken a bunch of grad level courses in statistics (as opposed to CS flavoured ML). A lot of Fisher's and Gosset's work was done in an era of "small" data. >>if you can figure out how a "classically" trained statistician can create an acurate model with: >>1) almost no data >>2) inaccurate data >>3) seasons in which the next one often bears no resemblance to the previous one Stats / ML can't help much with 1) and 2). But wrt 3), dealing with issues of non-stationarity is very much a subject of statistical literature.