The name of the game this week has been trade deadline talk. Monday's deadline created an interesting weekend of news, takes, and of course one voided trade. We won't really be touching specifically on the deadline as the moves and implications have been very well covered on the site. Start here for a rundown on all that happened, plus links for analysis on the fantasy relevant trades. Go here for coverage specific to cap leagues. And go here, and here for some Ramblings coverage and fall out.

Today we are going to return, as we occasionally do, to players' ability to put up consistent production. In the past we have used some of this data to talk about streaky players in a given season, and trying to see if we can find streaky players over the course of multiple seasons. I wanted to think about it a little differently today. I am thinking about managers who are fighting to win their current weeks and are staring at a bunch of free agents and wondering who to add. We already take into account a player's general performance (point pace), deployment/time on ice etc. Sometimes those things feel a bit like a coin flip. What if a team needs a single-game Hail Mary? Maybe it would be helpful to know if a player tends to get multi-point games. Or maybe they see a two-game schedule ahead and want a player who is more consistently putting up points for the best shot at getting something.

All that and more this week on Frozen Tool Forensics: Most Consistent Players

In order to look at this I pulled data from Frozen Tool's Consistency report. We have looked at this in the past, but there are some improvements we should mention here too. It has the typical info about a player (position, team, etc.), but then breaks out into games played, stat-per-game, and consistency metrics for points, multi-point games, goals, and assists. Today we are going to focus on point consistency, and multi-point consistency.

If we were to just sort by general consistency (defined as the percent of games where a player gets a point) all we see are the league's top scorers. This makes all of the sense. Players who score the most are going to score in the most games. What we need to do then is account for a player's point pace. Using the data set we can generate a trend that will allow us to find what a player's expected consistency rate is given their points per game, and then whether or not their actual consistency rate is better or worse than that rate.

First up we are going to take the 'more consistent' group. The table below is the top ten (or at least started as the top ten, but since a bunch of players were all essentially tied it expanded).

There are clearly some big names on this list (Nathan MacKinnon, Jonathan Huberdeau, Jake Guentzel, Elias Lindholm, Jack Eichel etc.). These players are all owned, and there really isn't anything actionable with them here. There are also some more fringe/bubble players, and these are players to take note of. I am a little surprised by how many defensemen are on this list, many of which are likely available in league's free agencies. On forward I am seeing Anthony Mantha, Troy Terry, and Travis Konecny.

The emphasis with these players is not necessarily that you should be running out to grab them, but that these players have historically pointed more frequently than others at their point pace meaning they are a little less boom/bust than others at their point pace

On the flip side we have players who are pointing in fewer games that we would have expected them to.

We will get into this in a moment, but this happens when players have either a couple of huge games, or tend to get more multi-point and zero-point games versus more frequent one point games.

 Nick Schmaltz is a perfect example of the former. His overall point pace at the moment is .98. Up until February 20th though he had only put up 22 points in 34 games (53ish full season pace). That included a whole lot of zero-point games. Then he went and put up eleven points in two games which will do wonders for anyone's full season point pace. He was reasonably productive for several games on either side of those big games, but that huge outburst dramatically changed his pace while still leaving him with a lot of zero-point games.

This general theory holds for everyone on this list, they have been more feast or famine than the consistent players list.

The last list I wanted to share gets at the difference between the one or two big game Schmaltz-y type player and players who consistently get multi-point games. This table is our top ten players who get two or more points more often than expected.

It contains some of the same players as our inconsistent list above, but you will notice Schmaltz is not on it. It also drops guys like Mason Marchment (who was just outside the top ten of the last list), Johan Larsson, Jeff Skinner, and even Mitch Marner (though he still ranks pretty well, in the multi-point game list, just outside the top ten) further down.

Of the two, I think this is the preferred list over the 'inconsistent list'. Our inconsistent list had guys like Schmaltz and Larsson who have been wildly valuable in one or two game increments once or twice in the year, whereas the above list is players who have repeatedly delivered multi-point games. If I am using these lists as a tie-breaker, I am viewing the players with a dramatic drop between the inconsistent list and the multi-point list with a lot of skepticism.

So what is the application here? Well for me if I am down in a matchup with only a game or two to add and I am looking at Trevor Zegras, Nick Suzuki, Troy Terry, and Travis Konecny (if we can assume for a moment their point paces and deployment are similar) I might lean toward Zegras and Suzuki as I need the big game to close the gap. If I am winning and want to maintain my lead, maybe I pick Terry or Konecny as they have delivered more single point games this season and so are more likely to get me something over that time period.

But let's also be really clear here. These differences are marginal and are much more likely to be defeated by the randomness that is fantasy hockey. The point I am trying to make is that this information, just like the home/away discussion from last week, can be one small piece of the puzzle to help us feel like we are making informed decisions, and might, just maybe, over the course of enough of these coin flips, mean a few more of them land our way.

That is all for this week

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