Player Consistency Rankings
Consistency is another piece of the puzzle that we want to consider when planning our draft strategies. Understanding whether or not we have consistent players are on our squad allows us to identify which categories our team could run into problems in and which ones we can lock in at the beginning of the week. If we put together a team that projects to have a high average SPG, that’s great, but that average doesn’t give us the full picture. If our best steals options are inconsistent producers in the category, then our weekly floor is going to be lower than we might think. It should be noted that a player whose value fluctuates a fair amount from game to game is not always a worse option than a more consistent performer who produces the same overall value over the course of the season. There are some situations where you would prefer a boom-or-bust option. If you are the underdog in a matchup, a boom-or-bust player may be the better option because if your team and your opponent’s team play to their averages you lose. Or if you are down three steals and are deciding between two 1.2 SPG players on the wire. In that scenario, I would want the player who has a better shot at getting three swipes, even if he also has a better shot at not recording a steal.
To determine how consistent a player was, I pulled all of the game logs from the 2020-2021 season for the top-125 players in nine-category leagues (excluding players who played fewer than 20 games). After pulling the game logs, I calculated the standard deviation of each player’s performance in each category and then divided that number by the player’s season average in the category. The resulting number tells how closely a player played to their season average in the category. For example, Luka Doncic averaged 27.73 PPG in 2020-2021. His standard deviation in the points category was 7.66247. If we take that standard deviation and divide it by his PPG (7.66247/27.73), we get 0.27635. That’s a low number (lower is better) and was good enough to rank him as one of the most consistent players in the points category. After calculating the consistency measure, I turned each number into a percentile to make this analysis easier to digest. Finally, I took the average of all of the players’ consistency percentiles to determine which players are the most consistent in category leagues. I performed this analysis for nine-category leagues and eight leagues and put together rankings for each punting strategy. You can find links to the punting strategy consistency rankings at the end of the analysis.
What to do with this information is up to you. It’s very useful for DFS, where you are chasing outlier performances, and not very useful for Roto leagues where you are always playing the long game. For H2H leagues, I personally prefer more consistent players, especially when it comes to my early-round studs. It makes projecting matchups easier, which I place a high value on. However, I’m not going to sacrifice a great early-round fit for my build for the sake of a more consistent team.
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