Thanks for the feedback! Definitely something I've struggled with too. Turnovers, and especially scores, result in massive values because they just don't happen that often compared to TDs for offensive players. I'll see if I can find a way to tone that down in the off-season. Thanks for the help!

Glad you like it! I created the model these cards use. How do you think it does in evaluating player performance? Always open to feedback if you have any!

Always happy to take feedback and answer any questions on the model, but hope these changes help make things a little clearer when using it as a tool to evaluate player performance.

The expected result is that PAR values will fall generally speaking, but the hope is that it will give a better impression of how that player is performing compared to a starting caliber player at their position.

Rather than using the true median, this new version of PAR will compare a player's Production Score to the median of all STARTING players at their position.

If a player received significant playing time, regardless of how they performed, they would be near the top of the league by PAR making it hard to tell how they're doing compared to other players.

Production Above Replacement (PAR) had been measured against the true median of ALL players in the dataset. While this helps to keep 50% of players above a PAR of 0 and 50% below, it doesn't give us much insight as to how a player is performing.

Good stat analysis is always welcome.

Oh. So that was a perfectly rational thought. Don't have tons of those.

Great speed down the wing and an absolute snipe. Would definitely not complain if we saw more of it!

This intro had me laughing way too hard at 7am. Looking forward to the live show this weekend!