Monthly Archives: January 2017

New forecast presentation – PWR by wins

I’m pleased to announce an improvement to the way I present PWR forecasts this year. There were two guiding principles to the design of the new presentation:

  • The question people are really asking until conference tournaments begin is, “what will it take for my team to make the playoffs (or finish top 4)?”
  • Everyone is interested in something a little different—some are fans of a single team and just care about that team, some want to check up on rivals, and some want to dig through all the data to look for interesting outcomes.

My forecast posts in past years gave some insight into what it takes for a team to make the playoffs, but was limited to the teams I chose or scenarios I found interesting. To help expand that analysis to all teams, I sought a useful way to present all the data.

The table on the PWR By Wins page shows you how many wins each team needs to likely finish at each PWR ranking. If you want more detail on a specific team, you can click a team name to see the probability curves of how likely that team is to end the regular season with each PWR ranking with a given number of wins out of its remaining scheduled games.

This is the first public presentation of this, so I’m sure there will be some tweaks and improvements in coming weeks. Check it out, and let me know if there’s anything I can do to make this data more useful to you.

What does it take for each team to finish at each PWR rankings: PWR By Wins

Methodology

The page notes when the forecast was last run (assume that it includes all games that had been completed as of that time).

Each forecast is based on at least one million monte carlo simulations of the games in the described period. For each simulation, the PairWise Ranking (PWR) is calculated and the results tallied. The probabilities presented in the forecasts are the share of simulations in which a particular outcome occurred.

The outcome of each game in each simulation is determined by random draw, with the probability of victory for each team set by their relative KRACH ratings. So, if the simulation set included a contest between team A with KRACH 300 and team B with KRACH 100, team A will win the game in very close to 75% of the simulations. I don’t simulate ties or home ice advantage.