## Gains, Losses, and the Human Response to Risk

From Michael Lewis’s new book, The Undoing Project (highly, highly recommend)

Imagine two scenarios with these choices:

Scenario 1:
Choice 1: \$500 in your pocket
Choice 2: 50% chance of \$1000 and a 50% chance at \$0

In this scenario, it’s extremely likely that you are going to opt for choice 1. There is no risk, you get \$500 and can walk away.

Scenario 2:
Choice 1: Lose \$500
Choice 2: 50% chance of losing \$1000 and 50% chance of losing \$0

In this scenario, I bet that most people would opt for Choice 2. The risk of losing more is worth the potential chance to lose nothing.

People respond to risk very differently when it involves losses than when it involves gains. In a sense that with a gain, people are ready to take the sure thing even though they could’ve risked it for more, and in losses, people would rather gamble and potentially lose more for the chance to lose nothing.

## Post Mortem: Player Projection Model

Well, this was disappointing. The same model showed some pretty good success in predicting the 15-16 season, but totally botched the 16-17 season.

Running the model to the 15-16 season, predicted goals had an r^2 of 0.3037 and predicted primary points had an r^2 of 0.3421. In this iteration, 80 forwards had their goals predicted correctly, 77 forwards had their primary points predicted correctly, and 36 players had both their goals and primary points predicted correctly.

For this season though, well, the model essentially failed. With the same weighting parameters and system, this season didn’t seem to cooperate. Goals had an r^2 of 0.1982, and primary points had an r^2 of 0.2952. Of the 298 players evaluated, the model hit on just 51 players for goals, 66 for primary points, and a measly 25 for both.

Issues with the model:

I think it’s too lenient. The way the predictions work, if you can recall, is similar to 538’s version of CARMELO. The issue, though, is that I don’t think I’m getting enough separation to make

each player unique. Each player, with the current weighting system, is coming away with too many comparable players. Throw all of these guys into the mix, and the  model will end up projecting very close to league average, even for the league’s better players.

## Free Rick Nash

[Unless explicitly stated otherwise, all stats for this post are via Corsica.Hockey]

It’s time for the Rangers, Alain Vigneault and co, to get Rick Nash off of the PK, and let him play his minutes 5v5 and 5v4 where he can be more effective.

It may come as a surprise to some, as Nash’s PK ability has been heralded since Babcock used him there in the 2010 Olympics, but no Rangers forward has been on the ice for more goals against on the PK, no forward has a higher GA/60, only Kevin Hayes has a worse xGA than Nash.

Is that totally the fault of Nash? Of course not, but, it is concerning, and it begs the question if he’s doing more harm than good to the Rangers on the PK. And this needs to be considered along with the detriment to Nash’s 5v5 time, where he’s consistently proven to be one of the league’s most efficient goal scorers.

Including this season dating back to 15-16, Rick Nash has skated 1,907 5v5 minutes, roughly 12.46 minutes per game and scored 25 goals. Sounds low, but consider that this ranks 4th among all Rangers forwards in those three seasons that meet a 1000 minute minimum (Grabner, Kreider, Brassard). If there’s something that Nash doesn’t have to prove, it’s his ability to score goals.

What should shock you most about the paragraph above is that Nash only averages 12.46 minutes a game of 5v5 time on ice. That is not enough, and I think part of it is because he is an all-situations player for the Rangers. I think it’s time that Nash, now 33, spends no time on the PK and instead gets his minutes in during 5v5 and PP time.

Let’s jump into how the TOI spreads for the Rangers look on a game by game basis…

Nash gets the 5th most TOI per game during 5v5 play on the team. If you’re using TOI as your identifier, these are borderline second-line minutes. It’s not enough.

(This data is via my own scrape)