Another year, another season, another Fantasy Football championship. Well, almost. We made it to the Finals for the second year in a row, but sadly lost to the 12-1 team in our league by a score of 144.7 to 156.6. Suddenly, my last post on match-ups now seems very perspicacious:

Everyone has had that one week where they score 150 points, only for their opponent to somehow put up 160).

Did I just quote myself so that I could use “perspicacious” in a sentence? Yes, yes I did. Interestingly enough, the eventual winner also won our league two years ago, meaning that the same two teams have combined for 3 out of the last 3 Championships and 4 out of the last 6 Finals appearances. Coincidence?

In this post, I will examine the importance of optimal play in Fantasy Football.

Defining Optimal Play

In the strictest sense, playing optimally means that a manager is maximizing his or her total points scored on a weekly basis. In a perfect world, this would entail picking up the best free agents by position and starting them throughout the course of the season (e.g. acquiring Jack Doyle (TE) from the waivers and starting him in Week 1 when he got 18.5 points, the highest point total for all TEs that week). While such a strategy is theoretically possible, the large amount of roster churn required as well as the significant amount of risk involved make this strategy almost impossible to implement in any sort of standard league.

A more practical interpretation of optimal play is a manager’s ability to choose the best starting lineup given his or her current roster for that week. By defining optimal play like this, we split the manager’s abilities into two distinct buckets: (1) all the actions required to arrive at his or her current roster (i.e. drafting, waiver wire acquisitions, trading) and (2) setting the best starting lineup. Isolating the impact of (2) is a relatively easy exercise to do in retrospect (since all the data is available), and that’s exactly what I did over the 13-week Regular Season.

Of course, it should go without saying that the subsequent analysis does not account for the inherent randomness in Fantasy Football (i.e. variability in player performance, match-ups/schedule, etc.)

Results

I reviewed the Regular Season performance (i.e. Weeks 1-13) for all 14 teams in our league and calculated each team’s hypothetical optimal score every week, as well as the number of points below optimal (PBO) that each team actually scored. The smaller the PBO, the better.

Here’s a sample of what that looked like in Excel:

The following tables show a summary of the actual, optimal, and PBO results for each team over the Regular Season:

The average PBO per week was 13.0 in our league, with a standard deviation of 3.4. Out of the 14 teams, 11 teams performed within one standard deviation of the mean. Team 8 had an average PBO per week of only 7.3, while Teams 7 and 9 were on the other end of the spectrum, boasting PBO values of 17.9 and 21.0 (more than two SDs out!), respectively. But did these outliers perform significantly better or worse in terms of Regular Season record?

Although the sample size is quite small, the answer appears to be “not really.” As we learned in my last post, Regular Season rank has a much stronger correlation with Total Points For than any other factor, and that is apparent in the scatter plot below. There was a -69.4% correlation between Regular Season rank and Total Points For (remember, it’s negative because a smaller rank denotes a better overall record), compared to only a 2.7% correlation between Regular Season rank and Total Points Below Optimal.

To further hit the point home, the league average Total Points For was 1,456.0. The six teams that made the Playoffs had an average of 1,512.3, while the eight teams that didn’t make the Playoffs had an average of 1,413.7. When it came to Total Points Below Optimal, however, there was virtually no difference between teams that made the Playoffs and those that didn’t: the league average Total PBO was 168.7, while Playoff teams averaged 168.5 and non-Playoff teams averaged 168.9.

The below table summarizes our league’s Regular Season rankings. In addition to each team’s Total PBO, I also calculated the Total PBO for each team’s opponents throughout the Regular Season. Lastly, I ran an alternate scenario where I assumed each team had the same schedule but played optimally throughout the entire Regular Season.

Concluding Remarks

While playing optimally may not be directly correlated with each team’s Regular Season record, it turns out that Total PBO for each team’s opponents was correlated -37.2% with that team’s record. Playoff teams’ average Opponent PBO was 187.8, compared to non-Playoff teams’ average of 154.5 (remember, overall average of 168.7). In other words, teams with better records “capitalized” on their opponents’ mistakes, and this had a non-trivial impact on the final Regular Season standings.

In addition, the optimal Regular Season scenario (last three columns of the table above) yielded some interesting results. If every team played optimally every week, then the top 10 teams over the Regular Season would have remain unchanged. However, due to the overall competitiveness of the league (six teams were either 8-5 or 7-6 in the Regular Season), an extra game won or lost in this scenario led to Teams 12 and 1 dropping out of the Playoffs and Teams 4 and 7 making it instead.

The “all optimal” scenario also indirectly highlights (once again) the painfulness of unlucky match-ups. Team 6, for example, had the second highest Total Points For but only ended up 5-8 and in 10th place. In the “all optimal” scenario, Team 6 once again boasted the second highest Total Points For but remained unchanged at 5-8 and 10th place. This is yet another argument for having more flexible Playoff seeding, as mentioned in my previous post:

Another alternative would be to reserve the last seed in the Playoffs for the team that made the top 6 in points scored during the Regular Season (assuming a 6 team Playoff format) but did not make the top 6 in win-loss record.

Interestingly, Team 8, which had the lowest PBO in the league, also benefited from having the second lowest Opponent PBO. Under the “all optimal” scenario, Team 8 would have dropped from having a 4-9 record and holding 11th place to just winning one game and being in dead last.

Before I sign off, let’s revisit our earlier decision to

split the manager’s abilities into two distinct buckets: (1) all the actions required to arrive at his or her current roster (i.e. drafting, waiver wire acquisitions, trading) and (2) setting the best starting lineup.

Stated simply, Bucket (1) measures a manager’s ability to maximize his or her Total Points For under an “all optimal” scenario. Inherent randomness aside, I would argue that this is largely a test of skill. Bucket (2) can be further split into two aspects: (A) the manager’s ability to set the optimal lineup every week (largely a test of skill) and (B) the ability of the manager’s opponents to play optimally (more a test of luck, as schedules/match-ups are in play).

As always, you can find my work here.