Fantasy Football 2024: FFNuThang 2020 Regular Season: A Retrospective

I thought it would be fun to gather this year's regular season FFNuThang data and examine how each of our teams performed--hopefully it'll become an annual tradition. I admit this post would have been more timely in December, but I needed some emotional recovery time. Still, maybe it's better late than never.

But before diving into the analysis, a shout-out is warranted. Big thanks to Tim for continuing to do what he does as commissioner and primary power ranker. This season more than others posed particular challenges with COVID-19 ravaging teams both fantasy and real. Nonetheless, Tim addressed these hurdles calmly and fairly, allowing the league to proceed with minimal disruption. Color me impressed and appreciative, if not surprised.

[Edited to add: at the risk of heaping too much praise on Tim, I think we're all also incredibly grateful his Thanksgiving-themed power rankings (maybe the best ever?), which offered such nuggets as:

[Aaron's] Thanksgiving Day Comp: Tofurkey. You fake, bro. Why do vegans like to try to make imitation food (tofurkey) that not only doesn’t taste like the actual food (turkey), but tastes worse than if you had just made that food how it should be made (tofu)? Sorry to any vegans out there, but tofurkey is no good. It tries to be a legit turkey, and thinks it’s a legit turkey (“easily a top 5 team”), but it’s actually and obviously not (bottom 5 in Points For). I would much rather eat some deliciously-stir-fried tofu by itself than some lame-and-overly-salted version of tofu-as-turkey.
[Steve's] Thanksgiving Day Comp: Paper Plates. First of all, it’s not even edible - that’s how bad Steve’s team has gotten. But I’m not even talking about the nice sturdy plates with designs on the edges and stuff. I’m talking about those plates that you have to stack three or four on top of each other to maybe get enough structural integrity. The ones where the food just falls off the side because there is no side lip, or the sauce gets soaked up immediately by the plate. The ones that work better as a kids craft supply than it does at being a plate. You know your Thanksgiving dinner is going to suck if you’re eating it off of basically a glorified paper towel.]

And now onto the data.

I've broken the post down into two parts:

Part A provides a series of charts/tables/graphs offering a broad data overview of the league as a whole.
Part B evaluates each team's performance over the season, examining their highs and lows and season trends.

Part A: Data Overview

(The images in this section are screenshots from data available here and here)

Weekly Average Win Probabilities

This chart is the result of my frustrations at the beginning of the season when I kept taking Ls despite my relatively high weekly point totals. In other words, I wasn't losing because my team was bad, but because I was getting unlucky match-ups.

To "remove" such luck in evaluating a team's quality, I thought it made sense to look at the probability that a team should win based on its point total compared to those of all other teams ("win probability"). For example, if a team scored the 2nd highest point total for a week, then its win probability for that week would be 90.91% (=10/11 because it would have beaten 10 of 11 other teams).

The below chart presents each team's weekly average win probability during the regular season.

Final Regular Season Ranking v. Probability Ranking

Tied to the previous chart, this one provides a comparison of each team's actual final season ranking against its ranking based on win probability. The "matchup luck" column reflects the difference between those two rankings (i.e., how many much a team's ranking dropped or rose as a result of luck).

Points Scored - Various Statistics

This chart lists each team's season point totals/averages (in descending order), as well as its median points scored and standard deviation in points scored over the season.

Season Trends - Weekly Point Totals and Weekly Win Probabilities

The following two graphs show each team's "points scored" and "win probability" trends over the season. The graphs in these forms are fairly unreadable, but you can see the disaggregated data on the Data Studios Report as well as in each team's summary below in Part B.

Part B: Individual Team Performances

Because this post focuses on regular season data, this section evaluates each team in order of their final regular season ranking.

1. choubacca

choubacca Highlights: regular season (and playoffs) champ; 2x high scorer for the week; “top 5” in each of win probability % (#5) and total points scored/weekly average (#3)
choubacca Lowlights: 1x low scorer for the week; bottom in standard deviation over the season (#12); not "top 5" in median weekly score
choubacca Summary: all's well that end's well, and the season certainly ended well for Aaron's team. Unsurprisingly, Aaron talked a lot of trash over the course of the season, sometimes with vague -- and therefore irrefutable -- statements like "I have a top 5 team, easily." And while that statement rightfully deserved scorn, Aaron's team was able to go big when it counted despite having the most erratic overall performance. For example, Aaron's week 12 win was not only the highest score of the week, but also the highest score for any week throughout the season and by a good amount (see first graph below). And Aaron's week 13 win (over me 😢) was crucial to earning his regular season title. So even though it hurts, I have to tip my hat to you, Aaron. Your team was a top 1 team, easily.

PS: if there's one thing I can take solace in, it's that I at least didn't trade Dalvin Cook for Aaron Jones.

Preseason Projections

PPS: I'll admit that Aaron already acknowledged that Dalvin Cook was better than Aaron Jones midway through the season.

2. Spider Pig

Spider Pig Highlights: 2x high scorer for the week; top 5 in each of win probability % (#4), total points scored/weekly average (#5), and median weekly score (#4)
Spider Pig Lowlights: near the bottom in standard deviation over the season (#10); biggest loss differential of all season (loss by 104 points)
Spider Pig Summary: Luke's team had a stronger start than finish, and you can see the general downward trend in his points scored over the season (first graph below). Still, his team was able to ride the highs from the beginning of the season into the playoffs, and punctuated the end of the regular season with a signature win over Jeff Chen's team. But it was a pyrrhic victory at best, paving the way for Aaron's team to claim the regular season crown (and ultimately the playoff championship).

Interestingly, Luke is also the source of Aaron's other major regular season highlight: biggest win differential (or loss differential) of all season, coming in at a whopping 104-point difference in week 12. For reference, the next biggest differential was 69.16 points (Aaron’s Week 7 win against Jeff, suggesting that Aaron has some control over which matchups in which he wants his team to show up). Of course much credit there is due to Aaron’s absurdly high score (157.3, highest all season across all teams), but much credit is also due to Luke's absurdly low score (53.3, second lowest all season across all teams). If I didn't know better, I -- and surely Jeff Chen -- would suspect some collusion. But of course I know better.

3. El Jefe

El Jefe Highlights: 2x high scorer for the week; top 5 in each of win probability % (#1), total points scored/weekly average (#2), median weekly score (#2), and standard deviation over the season (#5)
El Jefe Lowlights: 1x low scorer for the week
El Jefe Summary: Unlike Aaron's team, Jeff Chen's team really does have a true claim to being a "top 5 team" across all standard metrics. Looking at both weekly points scored and win probabilities, you can see the consistency in Jeff's team throughout the season (both graphs below). Outside of Jeff Chen's' week 7 stinker, it's pretty tough to find substantial faults in Jeff Chen's team. Based solely on objective measures, Jeff Chen's was arguably the best team in the league once matchup luck is removed from the equation. But luck is always in play, and an unfortunate week 12 matchup and a relatively poor week 13 showing cost him the regular season title (and a first round bye in the playoffs).

4. Terrific Tortoises

Terrific Tortoises Highlights: top 5 in each of win probability % (#2), total points scored/weekly average (#1), median weekly score (#1), and standard deviation over the season (#1)
Terrific Tortoises Lowlights: paying $32 FAB for Ryquell Armstead, who would never play
Terrific Tortoises Summary: I may be operating with blinders, but it's hard to me to find material weaknesses in my team. I think it's fair to say that my team is the only one that can compete with Jeff Chen's for the title of "best team in the league" after discounting for matchup luck. My team dominated across the objective metrics, not just by being in the "top 5," but by being top 2 in win probability, and then being the top team in every other category. True to its namesake, my team remained steady throughout the season. It only dropped below the 50% win probability mark twice throughout the season (first graph below), and remained consistently above league average in scoring (second graph below). But also true to its namesake, my team just couldn’t demonstrate hare-like scoring bursts. Although it never scored below 9th place in any week, it also never earned the highest score for any week. Maybe that inability to crank things up to a higher gear is what ultimately failed my team. Or maybe it was the unlucky matchup in week 13, on top of unlucky matchups in weeks 1, 3, 5, and 6.

But what good does it do me to stay fixated on bad luck? As I was stewing in my saltiness over how the season ended, I found inspiration in one DFWs tennis essays: "It turns out that a portion of the talent required to survive . . . is emotional. . . . When [tennis pro Michael Joyce] points out that there’s 'no point' getting exercised about unfairnesses you can’t control, I think what he’s really saying is that you either learn how not to get upset about it or you disappear from the Tour." So in that spirit, I'll stay focused on looking forward to next season.

5. Run It Back!

Run It Back! Highlights: 1x high scorer for the week; top 5 in each of win probability % (#3), total points scored/weekly average (#4), median weekly score (#3), and standard deviation over the season (#4)
Run It Back! Lowlights: 1x low scorer for the week
Run It Back! Summary: I stand by what I said before: people were sleeping on Gabs's team. It was one of the few to finish the season in the top 5 in across all objective metrics and showed an inverse trend to Luke's team. That is, Gabs's team had a slow start, but trended upward as the season progressed. In fact, when you only look at the stats for weeks 7-12, Gabs’s team was at the top of the league in each of win probability % (#1), total points scored/weekly average (#2), median weekly score (#1), and standard deviation (#3) (see table and chart below). Between having a strong start and a strong finish, the latter’s probably the better option and it seems fitting that Gabs's team prevailed over Luke's for 3rd place in the playoffs.

6. Tres Chicos

Tres Chicos Highlights: 3x high scorer for the week
Tres Chicos Lowlights: 2x low scorer for the week; bottom half in each of win probability % (#8), median weekly score (#9), and standard deviation over the season (#10)
Tres Chicos Summary: Jeff Lin's team was a roller coaster ride with high highs and low lows. While his team earned high scorer of the week more times than anyone else's (3x), it also had more than it's fair share of weeks as low scorer (2x). Of the teams that were in the top half in total points scored/weekly average (#6), Jeff Lin's team was the only one that wasn't in the top half in win probability % (#8). With an average win probability of 45.45%, Jeff Lin's team should have had a losing record if all luck were removed. In fact, in weeks 7, 8, 9, and 12, Jeff Lin walked away with wins even though the odds were against him based on points scored by the each other team in the league.

But who’s to let data get in the way of a good time. Jeff Lin’s team is best embodied by two mantras: (1) go big or go home and (2) it’s better to be lucky than good. And by going big when it counted and riding that luck, Jeff Lin’s team edged his way into the final playoff spot (and ultimately secured the silver medal in the playoffs). An impressive showing for Jeff Lin in his rookie season in the league.

7. 第十一

第十一 Highlights: top 5 in standard deviation over the season (#3)
第十一 Lowlights: paying $63 FAB for Nyheim Hines, only to drop him 3 weeks later
第十一 Summary: Abraham's team was at best the the poster child of a middling team. It was never the low scorer or high scorer for the week, and racked up fairly steady point totals throughout the season (with weeks 6-7 as outliers). His team just barely made the top half of the league in win probability % (#6), and just fell short of the top half in total points scored/weekly average (#7). His team's median weekly score was also just a hair below the league average of median weekly scores (98.26 v. 99.45). Abraham's team wasn't quite “第十一”, but also certainly wasn't “第一” -- really just “馬馬虎虎”.

8. #LetRussCook!

#LetRussCook! Highlights: 1x high score of the week; apt team name
#LetRussCook! Lowlights: 1x low score of the week; bottom half in each of win probability % (#11), total points scored/weekly average (#8), median weekly score (#11), and standard deviation over the season (#7).
#LetRussCook! Summary: The best part of Eric' team was probably his team name. Unquestionably, Russ Cooked pretty much every week of the season or at the very least kept things on a simmer. Unfortunately, the rest of Eric’s team was cooked. A few of his players showed sparks here and there, but generally couldn’t offer consistent production. That said, Eric's team managed to stay pretty clear of Sacko throughout the season, despite his bottom-of-the-barrel win probability %. So at least there's that bright spot.

9. Kupp of Joe

Kupp of Joe Highlights: not Sacko; blockbuster trade to get CMC
Kupp of Joe Lowlights: 1x low scorer of the week; bottom half in each of win probability % (#7), total points scored/weekly average (#11), median weekly score (#8), and standard deviation over the season (#8); blockbuster trade to get CMC
Kupp of Joe Summary: Andrew's team had a some solid runs in weeks 3-5 and 8-10, but was a wreck for almost every other week in the season. His week 12 nadir wasn't just the lowest score of the week (46.6, which was less than 30% of Aaron’s score the same week), but was the absolute lowest score all season across the league.

To his credit, Andrew knew he had to do something big to salvage his season and swung for the fences in week 10 with a trade for CMC. That took mettle and you've got to respect it. Andrew's team might best embody the mantra "high risk, high reward," but in this case it fell victim to that high risk, ultimately housing a CMC who wouldn't suit up for the remainder of the season. Sometimes you just don't have either option of being good or being lucky.

10. Maybabyboo

Maybabyboo Highlights: Top half in standard deviation over the season (#6)
Maybabyboo Lowlights: 2x low score of the week; bottom half in each of win probability (#10), total points scored/weekly average (#9), and median weekly score (#10)
Maybabyboo Summary: On the bright side, Laura’s team didn’t have to fight against bad luck. Her team’s chance at winning was above 50% for 5 weeks and her team actually won 5 weeks. Her team’s final regular season rank matched exactly her team’s “probability” rank.

On the flip side, her team’s final regular season rank matched exactly her team’s “probability” rank. Her team’s chance at winning was below 50% for 8 weeks and her team actually lost 8 weeks. On a week-by-week basis, Laura's team regularly scored below league average, and in fact failed to top the league average in any week after week 7.* Laura has better seasons to look forward to.

*Note: Laura's team did score above the league median in week 11, but the league average was skewed higher because three teams had significantly higher scores than the rest of the league.

11. Unicorn QueenZ

Unicorn QueenZ Highlights: 1x scoring champ; near the top of the league in standard deviation over the season (#2); not Sacko
Unicorn QueenZ Lowlights: bottom half in each of win probability % (#9), total points scored/weekly average (#10), and median weekly score (#7); flirted with Sacko for most of the season
Unicorn QueenZ Summary: Although Tim's team was consistent, it was consistently in the bottom half of the league. I mean, just look at the graphs below--only one team might look at it with envy.

Still, I'd bet Tim was absolutely giddy by the end of the regular season. They say that bronze medalists are happier than silver medalists because, while the former is just happy to have placed, the latter can't shake the feeling of how close they were to winning the gold. With that same principle in mind, I'm sure Tim couldn't stop thinking about how he was spared the Sacko title, despite how close he'd come.

12. Chicken dinner

Chicken dinner Highlights: 1x high scorer for the week
Chicken dinner Lowlights: 4x low scorer for the week; bottom of the barrel in each of win probability % (#12), total points scored/weekly average (#12), median weekly score (#12), and standard deviation (#11); Sacko
Chicken dinner Summary: I mean...just look at those graphs. This clearly wasn't Steve's year. He was the low scorer of the week twice as often as any other team, and only scored above the league average in three weeks out of the entire season. Outside of week 4, Steve's team didn't have a strong start to the season, yet still managed to show a downward trend as the season progressed (see second graph below).

I like Steve. He's a great guy and it brings me joy to see him. I also understand that he got into stock trading this year, so I hope that’s going well for him. But I also hope that he’s training up for the NFL combine drills because we’re going to hold him to it and save the video forever.