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2019 Game Level Similarity Projections: Calculating Weekly Fantasy Point Expectations

Dave Caban explains the process used to generate the weekly projections that will power the apps in the 2019 season.

Throughout the 2019 season, I’ll be generating Game Level Similarity Projections (GLSP) on a weekly basis. GLSP use historical data to determine a realistic range of outcomes for a particular player in a particular game. These projections will be included in a number of our apps this season. With this in mind, let’s take a quick look at how these projections are built.

Saquon Barkley Vs. Dallas

Saquon Barkley will line up opposite the Dallas Cowboys to open the 2019 season. Barkley is an absolute monster and in an average game, I’d expect him to rush for somewhere near 85 yards while adding four to five receptions and half a touchdown. Given this line, Barkley should score approximately 22 fantasy points in PPR leagues.

When creating that stat line, I didn’t really consider the quality or attributes of the Cowboy’s defense. Doing so would likely allow me to refine this projection and help me to get a better sense of his median outcome. As a rookie, he faced Dallas twice scoring 25 and 24 fantasy points. Perhaps my quick and dirty projection above wasn’t bullish enough. Of course, it’s hard to draw conclusions from a two-game sample.

While this baseline expectation is somewhat useful, we oftentimes find ourselves interested in a player’s weekly range of outcomes. If Barkley were to really go bananas, how many points are we talking? If he struggles, how poor of a game could he have? While we only have data from two games in 2018 to work with, we can modify our search to create a larger sample. To do this, we can search for players similar to Barkley, and review their performances when facing defenses similar to the Cowboys. This is the goal of the GLSP process.

The GLSP Process

A ton of calculations are made in the GLSP process but overall it’s actually pretty simple. Let’s take it a chunk at a time:

Step 1 – Finding Matches for Player In Question

  • On a positional basis, determine the statistics that are most heavily correlated with fantasy scoring as well as the statistics that are most likely to carry from week to week
    • Weight the correlated statistics in a way the prioritizes those that are heavily correlated with fantasy points and are likely to carry from week to week.
  • Create an average stat line of the last eight games played for the player in question.
    • Review the last eight games played and remove/replace any in which the player’s volume (passing attempts, rushing attempts, or targets, depending upon position) are significantly lower than average.
      • For example, a game in which a quarterback who generally throws more than 20 passes per game records just eight will be removed and replaced with an older game in which the threshold was met.
        • Only games within the last 20 regular season weeks are considered.
  • Compare the difference between the average stat line generated via the above for the player in question with the end-of-season stat line created by every player from the same position throughout the past five seasons.
    • Use weightings to determine an aggregate difference and then rank these differences.

Step 2 – Finding Matches for Opposing Defense

  • Using the statistics identified in Step 1, compare the average stat line of the defense being opposed, from its eight most recent games, against the season-ending stat lines of every defense in the last five seasons.
    • Use weightings to determine an aggregate difference and then rank these differences.

Step 3 – Combine Steps 1 and Step 2

  • Having identified players that are similar to Barkley and defenses similar to the Cowboys, search for games in which players similar to Barkley opposed defenses similar to the Cowboys.

Step 4 – Review Player Performance in Matching Games

  • Compile the 50 best matching games and review the results for those in which the matching player did not meet required opportunity thresholds.
    • Remove such players from the analysis.
  •   Review and compile the fantasy points produced by the matching players in the matching games and gather a range of outcomes.

Barkley’s Week 1 Range of Outcomes

Let’s walk through the above, starting at Step 2,  and use the specifics relevant to Barkley.

Barkley’s last eight games game occurred between Weeks 10 and 17 of the 2018 season. He saw ample opportunity in all of these games. As a result, all eight games will be used to generate his average stat line.

Year Week Att Yds TD Targets Rec Yds TD FP
2018 17 17 109 1 8 4 33 0 24.2
2018 16 21 43 1 7 5 34 0 18.7
2018 15 14 31 0 10 4 25 0 9.6
2018 14 14 170 1 5 4 27 0 29.7
2018 13 24 125 0 4 3 21 0 17.6
2018 12 13 101 1 8 7 41 1 33.2
2018 11 27 142 2 3 2 10 1 35.2
2018 10 20 67 0 5 4 33 0 14
Average 19 99 0.75 6 4 28 0.25 22.8

Given this stat line, and the weightings assigned to each statistic Barkley’s best match is 2015 Jamaal Charles.1 The former Chief rushed 15 times per game for 73 yards, and 0.8 touchdowns in 2015 while also recording six targets, four receptions, 35 receiving yards, 0.2 receiving touchdowns and 21 points per game. In Weeks 4 and 5 of that season, he faced the Bengals and Broncos. As the 2015 Bengals rank 5th overall among matching defenses and the 2015 Broncos rank 10th, both contests are among his closest matches.

As a result, they will factor into Barkley’s average GLSP stat line and fantasy points range of outcomes created.

Final Numbers

In the 50 strongest matching games in which a player like Barkley opposed a defense similar to Dallas the below stat line was averaged.

Att Yds Yds/Att TD Targets Rec Yds TD FP
16.7 77.1 4.5 0.6 5.9 4.5 32.8 0.1 19.5

More importantly, the 25th percentile of the PPR points produced by his matches equals 14.6, the 50th percentile is equivalent to 18.7, and the 75th percentile is equal to 25.3! Less than 3% of his matches scored under 10 points. Just 14% were held between 10 and 15, 33% scored between 15 and 20, 12% scored between 20 and 25, and a whopping 27% scored more than 25 points.

Remember, GLSP projections are agnostic of player health, situation, or any other external factors. I’ll be posting the Week 1 numbers in a follow-up article.

Image Credit: Mark Goldman/Icon Sportswire. Pictured: Saquon Barkley.

  1. Note that the above is not inclusive of all stats considered.  (back)

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