NBA Schedule Artifacts and Player Fantasy Scoring

The NBA schedule is long, grueling, and comes with all sorts of decision points for managers filling out their 30 games in an Ottoneu lineup. Should you play someone on a back to back that is their team’s 3rd game in 4 days? Should you take a chance on a player stepping into the starting lineup? Should you worry about playing Spurs players when they go on the road for weeks while the rodeo is in town? Should you target weak opposing defenses?

Starting the insight process into these questions is the goal of this post. I won’t cover each one here, but hope to get to them in the future. This analysis will use traditional fantasy points as the basis despite lineup decisions being the toughest in categories leagues since TFP tend to be a midpoint of sorts between categories and simple points as well as more easily translated to the game level than categories. I’m going to look at the performance of the top 150 players by total points scored during the entire NBA season (not just the period covering the Ottoneu season - a question I’d like to revisit another time) since these are the players most likely to be relevant in Ottoneu and it is roughly equal to the 144 players I use to set replacement level for my values.

Home vs Away


(note: dashed line is y=x in the left plot and the mean differential in the right plot)

Looking at the aggregate level we see the average player in our pool scores more at home than away, which is expected since home court advantage for teams has long been established and if the home teams are winning, then their players are playing better. The range for the distribution of the differences is also shifted more towards home scoring, with the x-axis reaching to +10 and only -6 or so, though the percentage of players occupying those extremes is fairly small, particularly in the positive direction.

The majority of the players fall into the range around -4 to +5, which isn’t a huge difference but could add up if you have a lot of players on road trips in a given week. It depends on each team’s makeup, depth, and each player’s schedule, but there are scenarios where it makes sense when picking your fringe starters to go with the player with home games.

Individual Players

Diving into the specific players with the largest splits we see quite a few stars peppering the top 10 list for home cooking alongside a few role players. To me this is indicative of the potential home scorekeeper bias common in the NBA where star players tend to get crediting more leniently in statistics such as assists (+2 traditional fantasy points), blocks (+4), and steals (+4).

Embiid and Mitchell are the two biggest stars on the list and while neither played a full season, they both got more assists at home than on the road. Embiid received a significant boost in stocks (steals + blocks), recording 3.25 per game in 24 home games versus 2.27 in 15 home games - a four point boost by itself. Mitchell, meanwhile, did it simply through improved scoring as he put up nearly 6 more points per game at home and with better efficiency than he did away.

We also have a few true role players in O’Neale, Drummond, and Pritchard, and they’re all pretty straightforward cases. Drummond and O’Neale both shot the ball way better at home and recording stock totals at least 50% higher than their away games (note this does not discriminate between O’Neale’s time in Brooklyn and Phoenix). Pritchard shot the ball better at home as well, and also benefits from playing on a team that went 37-4 at home.

The last group here is the players who are clearly above replacement level but maybe not worth $50+. It’s a large range and not a well-defined one either since Sengun in particular may belong in the group with Embiid and Mitchell instead. Jerami Grant and Mikal Bridges stand out as players who are just barely above the universal replacement level of about 22 points when they’re away from home. Playing Royce O’Neale, who was nearly universally a $1 player, during home games would have been nearly as good as playing either Grant or Bridges, who both go for $20+, when they were away.

Of course, Ottoneu uses head to head matchups instead of season-long play so the schedule needs to line up to take advantage of these situations.

Oddly enough we again see plenty of stars for the players who do better on the road compared to at home. Most of the discrepancies for them (Giannis, KAT, Kawhi, Beal, Booker) seem to come down to
simply shooting better away from home. For those wondering about the third Suns’ star, KD performed about the same home and away.

It is interesting to see LeVert, Oubre, Thompson, and Richards and Rozier show up on this list since they all had star-level teammates show up in the previous chart. However, it’s tough to draw any conclusions based solely on these charts since many of those players dealt with their own injuries (or a trade for Rozier). Perhaps it’s related to teams trying to stay competitive on the road and not dipping into their deep bench as much since each of these players is certainly in the rotation for their team and sees an uptick in away minutes here.

Back to Back


(note: dashed line is y=x in the left plot and the mean differential in the right plot)

Now we turn to another scheduling factor to consider - the back to back. While the home / away analysis was a fairly balanced dataset (5323 home games vs 5315 away), back to backs are far less common (8970 non-B2B games vs 1668 B2B) and the NBA continues to try and minimize them when creating the schedule. They have a pronounced impact on some players and even have a selection bias going on for this analysis since teams hold some players out on back to backs for various injury and load management reasons, such as how Al Horford didn’t play in both games of a back to back once all year.

All that being said, we do see a mean difference of barely less than 0 points and a distribution that is more symmetric than the home / away one, though this time the left tail goes further out from zero than the right tail. The distribution is overall more spread out than the home / away one with a smaller peak (~0.08 compared to ~0.10) for the maximum percentage of players at a point in the curve and a range stretching from -15 to +12 here.

Individual Players

We’ll start off with the players who performed better on a back to back, and here I’ve included some additional information with the number of games in each sample and the average rank of their opponents’ defensive rating to help inform our takeaways.

Immediately we see the effect of Horford sitting out the back end of a back to back with Payton Pritchard and Jayson Tatum upping their games, though they also benefited from facing poor opponents on these back to backs. Other players who benefited from their teammates sitting games were Herb Jones (Zion Williamson), Bobby Portis (Khris Middleton), Tre Jones (Victor Wembanyama), and Rui Hachimura (LeBron James and a little bit Anthony Davis). Tatum and Tre Jones also pulled off the tough task of increasing their output while seeing their minutes decrease by over one per game.

There aren’t clear explanations for Grant, Duren, and Hardaway Jr to me, but if I’m missing something, let me know!

Now to the players who dropped off and it’s headlined by three guards in Jamal Murray, Donovan Mitchell, and Immanuel Quickley. Murray dealt with minutes restrictions on back to backs as he ramped up from injury, though he still managed an above replacement level 26.6 FPTS. Quickley struggled at times with his increased responsibilities after his deal to Toronto, which is reflected here.

Porzingis can more or less be tossed out of this dataset since he only played in 3 B2Bs all year - Boston often had him sit the first one when Horford played before sitting Horford in the second - and one of them he only played for 6 minutes and 14 seconds.

Booker has seen a dropoff the last two years, but in the 2021-22 season he was amazing in 10 B2Bs. Randle has dropped off in general on B2Bs the last two seasons, and particularly in steals and blocks. It’s a similar story for Adebayo, though he also suffered from a lack of Jimmy Butler (only played in 6 B2Bs).

Chet Holmgren is the last player I want to mention since he saw a large drop off, which isn’t surprising considering his lean frame and his status as a rookie. Although I don’t have any research to back this up, I would imagine that both of these factors were working against him on B2Bs. However, despite tying for the most appearances in B2Bs of the 20 players listed in these charts, the 14 B2Bs for the Thunder last year are about average for an NBA schedule.


I dove into some of the statistical artifacts from this past basketball season based on dimensions of the schedule to see how players performed in different situations and saw it can impact lineup strength quite a bit. It could change how you fill out your lineup, particularly the UTIL spots in weeks where maybe the stars aren’t playing as much and team depth is tested. It’s also another situation where finding a handcuff of sorts is beneficial, as when a team sits a player consistently on B2Bs, other players will step up. Or if you know a player steps up at home, maybe you can steal a start with them to fill out the lineup.

However, at this point there aren’t any firm conclusions as these are all summary statistics and I haven’t done any work to see how this translates from year to year. I’d like to do some work there to see if we can expect players to bounce back from one year to the next, if particular locations influence the home / road splits, and other insights.

Before that, though, I’m planning to finish off the summary stats with a look at how players perform based on their opponent’s defensive rating rank to hopefully gather some insight on which players beat up on weak teams and if using opponent defense strength as a tiebreaker makes sense when picking starters.


Time to look into player performance vs opponent defensive rating rank. All the previous caveats about predictive ability and translating from one season to the next apply here, as well as an additional layer of subjectivity since I grouped defenses into three buckets where the top 8 defenses are “good”, the middle 14 are “mid”, and the bottom 8 are “bad”. Is there any technical reasoning behind the 8 / 14 / 8 buckets? Nope. It just seemed like neither 10 / 10 / 10 nor 5 / 20 / 5 fit as well.

I’m also using the defensive rating rank and not the rating itself, but maybe adjusting for defense strength is a project for some point in the future. It could also be useful to look at opponent net rating or win percentage since traditional fantasy points do include steals and blocks. Lastly, I’m comparing two metrics with different bases as defensive rating is a per 100 possessions metric and fantasy points are per game, which could impact the relationship between the two variables.

Good vs Bum


(note: dashed line is y=x in the left plot and the mean differential in the right plot. blue line in the left plot is the line of best fit)

Surprise surprise, players score more on average against the bums of the league than against good teams, though there are some players who step it up. The mean difference in scoring against good teams vs bums on a per player basis is -4.39, though the distribution is more uneven than those we saw in the previous post. Although five of the six highest scoring players sit below the y=x line, all six are above our line of best fit, indicating that they at least seem robust enough to be depended on against quality opponents.

Individual Players

Despite his poor showing in the NBA finals, Kyrie Irving sits atop our first leaderboard as he scored nearly 10 extra FP against defenses ranked in the top 8 than those in the bottom 8. Paul Reed comes up next with 8 more FP against the good teams, which is the biggest surprise to me of the entire table. He and Kristaps Porzingis stand out as the only two bigs on the list and my hypothesis there is that it’s harder to deliver the ball to bigs against better defenses.

We also see two pairs of teammates, though of wildly different calibers, with TJ McConnell and Aaron Nesmith of the Pacers and Devin Booker and Bradley Beal of the Suns showing up. The Pacers’ duo both played more minutes against good defenses, and I’m curious how the games Tyrese Haliburton missed stack up by opponent defense.

Nesmith and Reed are easily the lowest usage players in the table, checking in at around 16% usage for each, while Vassell and McConnell are the next lowest all the way up around 23%. Darius Garland is also a bit surprising to me since small guards typically struggle more in the playoffs and Garland is no exception there, but he was able to step it up in the regular season against good defenses.

Desmond Bane, wow! He only played about half the season and barely got to share the floor with Ja Morant this season, perhaps inflating his numbers against bums while showing his (and the beat up Grizzlies’) limitations against good teams. Two of the next four are members of the New York Knickerbockers, which was quite surprising to me, especially that Jalen Brunson was included after his amazing playoff performance. I’ll note that neither the Sixers (11th) nor the Pacers (24th) rank as good regular season defenses here, though Embiid did miss much of the season. Brunson stands out as the only player to perform better against the mid teams than the bums.

Once again it’s a list dominated by perimeter players, with Julius Randle the only player you could traditionally call a “big”. We do see some young perimeter players at the bottom of the table as Anthony Edwards, Trey Murphy III, and Jalen Suggs were all still on their rookie contracts last year. I’m curious if bumslaying is any sort of indicator that a player’s scoring against higher caliber defenses will catch up to their exploits against the dregs of the league - perhaps something to check on during future work.

Good vs Mid


(note: dashed line is y=x in the left plot and the mean differential in the right plot. blue line in the left plot is the line of best fit)

Here we have the tightest distribution of the three comparisons, which can be seen in both the scatter plot and the distribution of differentials. We also see the mean differential move closer to zero and the line of best fit edges up towards the y=x line.

Individual Players

Davis and Embiid lead the way here and despite “beating up” on mid tier defenses, their marks against those defenses were still extremely good. Beal, Garland, Vassell, and Irving show up again here with their performances against good defenses outpacing their numbers against mid and bum defenses enough to pop up in both tables.

Keegan Murray, Tre Jones, and Grayson Allen were varying degrees of role players last season, but were able to step up against better defenses. Jones has the benefit of playing next to Wembanyama and it would be interesting to see his splits before and after he entered the starting lineup.

SGA was simply very good against all teams.

Trae Young and Damian Lillard really stepped it up against mid tier defenses for whatever reason. We see more repeats with Brunson, Bane, and Randle all dropping off when shifting from mid tier to good defenses. Tyrese Haliburton narrowly missed joining them as repeats with his (good - bum) differential of -8.9.

Cam Thomas, Immanuel Quickley, and John Collins have very similar numbers overall, though of course Thomas and Quickley are guards who often initiate while Collins is a big. Jarrett Allen was very consistent against mid and bum defenses, but dropped off against good teams. Perhaps Garland picking up the slack against better defenses impacted his numbers.

Mid vs Bum


(note: dashed line is y=x in the left plot and the mean differential in the right plot. blue line in the left plot is the line of best fit)

This comparison fits in between the previous two, with the mean differential being about the same as that of the (good vs mid) comparison but having a wider range. Unlike the earlier graphs, the right most dots on the scatter plot have not all remained above the line of best fit.

Individual Players

Nearly every player in this table has appeared in another chart already, with just Steph Curry and Jalen Johnson not showing up earlier. Not much to add there, though the Jalen Johnson breakout sure seems real considering he did well against good, mid, and bum defenses.

Again we have a lot of repeat players with Embiid, Bane, George, and Suggs showing up here, though Embiid’s appearance was in the (good vs mid) chart while George and Suggs were in (mid vs bum) and Bane has shown up in all three. Kevin Durant, like Embiid, was good against all three categories but simply the least good against mid tier defenses.

The group of Jalen Duren, Deandre Ayton, and Daniel Gafford is interesting as three bigs who all rely on their guards quite a bit and played on bad teams last season (or at least most of the season in Gafford’s case). Could Duren be about to explode with a better spaced floor around him thanks to vet additions like Tim Hardaway Jr., Tobias Harris, Malik Beasley (plus a full season of Simone Fontecchio) and growth from Cade, Ivey, and Ausar Thompson? It could depend on his ability to win over JB Bickerstaff’s trust on the defensive end, but it seems like a distinct possibility.

Gafford will likely be capped in effectiveness this season as he splits time with Dereck Lively II while Ayton will be mentoring Donovan Clingan and hoping for improvement from Scoot Henderson, a full season from Anfernee Simons, and both from Shaedon Sharpe. The guard room could make Ayton look better in real life while capping his opportunities in fantasy either through improvement or more stagnation.

Amen Thompson and Jalen Green are two young Rockets who performed best against bum defenses and improvement from them against better foes will help Houston return to the playoffs next season.


The data bears out the hypothesis that players perform better against worse defenses, as players drop about 4.4 points when facing a good defense instead of a bad one and just over 2 points when transitioning from bum to mid or mid to good. As with my previous entry to this series, it’s tough to say exactly how much this will impact any sort of lineup choices managers face during the season, but it could be a tiebreaker for those last starts.

Similarly to the home / away and back to back splits, I’d like to dive deeper into this area with year to year consistency and refine the approach. I took a player-centric view here since it seemed like an easier subject to structure an initial post about, but would like to look more at which types of players are affected by opposing defense strength and maybe develop ideas for how much different positions or production tiers are impacted as defenses improve.

TBD on when that happens since I think I’ll bounce back to some arbitration-focused work as well as some basketball analysis that isn’t directly Ottobasket related.

1 Like