## 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)

## Re-Tooling the Rangers in one Off-Season

Note: The more I wrote in this blog, the more I hated it. It’s not that I necessarily dislike the team that I’ve built, it’s more of the hesitation that I know basically everything I wrote up isn’t going to happen. There is no doubt that the New York Rangers need change. There is no doubt that the FO needs to give AV the players he needs to succeed. There is no doubt that a few players, for lack of a better term, need to be forced off of this team for the Rangers to succeed. At the end of writing this up, I didn’t even want to post it, but I think that’s sort of the opposite reason that blogs even exist. So. Enjoy.

For the first time in my life, I don’t envy the General Manager of the New York Rangers. Jeff Gorton, though being in the Rangers organization since 2007, inherited a team from Glen Sather prior to the 15-16 season that was broken. Even within the Rangers, it didn’t sound like there was much confidence in the team this season. How many times to Alain Vigneault go on the record saying that this team deserves “one more kick at the can”? And that’s all well and good. Having loyalty to a core of players that has given you everything they can doesn’t go unnoticed among player circles.

But at what cost?

The 2015-2016 season for the New York Rangers was a failure. From top to bottom. Front office to coaching to the players on the ice. It’s easy to say it just wasn’t their year, but it is more than that. The core went rotten. The Rangers need a re-tool.

Because for some reason, no one else is.

Stats and visualizations for this post from corsica.hockey, RK_Stimp, and hockyviz.com

It’s at this moment that I’ve realized that some people reading this post might not even know who Jakub Nakladal is. On the one hand, that’s good, he’ll be a huge bargain as an unrestricted free agent this season to a wise team. On the other hand, it can’t be fun for Nakladal himself or his agent.

Nakladal is a 28 year old defenseman who shoots right handed, has size (6’2″, 212lbs), and hails from the Czech Republic. Place of citizenship is important because Nakladal has been selected to represent the Czech Republic at this summer’s World Cup of Hockey where he will undoubtedly prove right the team that signs him in July.

The 2015-2016 season was Nakladal’s first in North America splitting time between the Calgary Flames and their AHL affiliate Stockton Heat. On the surface, Nakladal’s numbers aren’t that sexy. 5 points in 27 NHL games, 14 points in 35 AHL games.

My argument is that there is Anton Stralman levels of untapped potential in Nakladal.

Nakladal ended the season in Calgary, and his overview chart is as follows:

## Applying Sabermetrics to Hockey

As we dig deeper and deeper into hockey analytics, the wise move would be to continue looking at baseball Sabermetrics for inspiration. Now, obviously baseball and hockey are two completely different sports, especially when we try to measure them, One thing remains inherently equal though. Runs lead to wins in baseball. Goals lead to wins in hockey. It’s the deeper digging that will separate the sports further (what leads to runs or preventing runs versus what leads to goals or preventing goals), but the root remains the same. You want to maximize runs for. You want to maximize goals for.

At the basis of this core in baseball, sabermatricians have come up with ways to calculate expected winning percentage based off of run totals. These analyses have been proven to correlate VERY highly to actual winning percentage. Which led to my curiosity: Can we substitute runs for goals scored in these evaluation tools, and see the same correlations?

Bill James:

Bill James’ expected winning percentage formula is based off the pythagorean theorem, and is widely recognized as one of the most accurate winning percentage calculators in baseball. At first, the formula was (R^2) / (R^2+RA^2).

## When Do NHL Forwards Regress?

Something that has puzzled me since learning about Nate Silver’s PECOTA for baseball, where they can predict how a player’s career will play out based on previous similar players of that caliber, has been at what age do hockey players actually regress?

[that was a very, very baseline description of PECOTA]

Often we hear that hockey players are in their prime from ages 27-31, and it’s downhill from there; but is that really the case?

In order to find out, based purely on their ability to compile points, I took a dive into the stats to find out.

Using only forwards, since this is a scoring metric, who have played in 10 or more games in a given season (since the lockout), I compiled the points/60 for each age that came up in the analysis (18-43). The ten game mark was used to remove any noise from players aged 18 who only appeared in 9 games before their NHL team opted to return them to Juniors as to not burn a year of their ELC.

On a quick full run, here’s what I found:

What does this tell us? Well, nothing, really. If you took this at face value and ran with it, you might come away thinking that hockey players just don’t regress as we thought they did.

## Why Alain Vigneault Failed the Rangers

After a day to reflect on the end of the New York Rangers 2015-2016 campaign, my initial thought has not been swayed. Coach Alain Vigneault failed the New York Rangers organization this season.

There are a few reasons that stick out on specifically how AV failed this year. Some easy to prove, others not as easy.

In no particular order but how they pop into my mind, I’d say the following are the most egregious in my opinion: