Every year I compile a bunch of statistics from around the NBA stats world and try to use them to project win totals for each team. Much like ESPN, I try to improve the process every year, and have settled onto a basic approach over the past few seasons, with the difference being which statistics I look at.

For a detailed breakdown of the process and a look at the results from last year, take a look at last year’s article. I’ll give a quick summary here as a refresher though, if you don’t want or need the full breakdown again.

First off, the statistics. For every iteration of these projections, I’ve used Win Shares (WS, from Basketball Reference) as the proxy for production (which personally I differentiate from impact, two different ways to evaluate players). Production is the ability of a player to put up raw points, rebounds, assists, with good efficiency and without committing turnovers. Win shares are a catch all box score aggregation statistic meant to tie those production values into a single measure that represents each player’s contribution to generating wins.

For impact stats, I started with just using Box-Plus-Minus (BPM) from Basketball-Reference, as it was easy to cull from the same site. Last year I added Real Plus Minus (RPM) from ESPN. Both are box-score correlated impact stats. They use adjusted plus minus data over several seasons to evaluate the impact each player has had on winning their games (in other words, what the team’s point differential is with each player on the floor, adjusted for quality of teammates and quality of opposition), then correlate box score values to that data to be able to generate more static numbers from single season samples. They aren’t great stats, so this year I’m adding one more, a new one from Jacob Goldstein called Player Impact Plus Minus (PIPM). It’s got more actual plus-minus data in it, relying less on box score priors, as far as I can tell anyway. The values assigned to different players also align more with my personal opinions, so there’s also a little bit of confirmation bias in why I like the stat.

The past two seasons, the projections have been off for the Raptors — last year we under-predicted their success, mainly because there was little data on the young bench players who made up a large portion of the rotation. The year before, we over-predicted their success, mostly because they suffered injury upon injury in the second half of the year, and of course there was a mid-season trade to muddy the waters. But in both cases, Win Shares was the closest win predictor, so my faith in BPM and RPM is reduced. I will still use them, but will weight them less this year.

The Method

The idea will be to generate a win projection from each of the four stats. Then I will weight the projections as 50% production and 50% impact. Win Shares will be the production statistic. The impact projection is made up of the BPM, RPM and PIPM statistics, which because I like PIPM so much, will be weighted at 25% BPM, 25% RPM and 50% PIPM. PIPM’s win projection also falls closest to the Win Shares projection, giving me even more faith in that impact statistic.

As with prior years, I will attempt to keep my paws out of the process as much as possible once it is settled on. Meaning that I won’t be making rotation decisions for teams, nor deciding that one player had an off year and to use different data for them (with only extreme case exceptions to that rule).

So, the mostly automated process is as follows. Each team’s roster is assembled based on contracts signed so far. Each player is assigned exactly the same minutes played as the year prior, the team’s total minutes are summed, and if they exceed or fall short of 19,680 minutes (48 minutes times five positions times 82 games), every player on the team is adjusted up or down proportionally to make that total match. The exception being that every player is capped at 3,000 minutes maximum, as any more than that is unreasonable on the face of it.

Once the new expected minutes played for each player are assigned, they also get their WS/48 (per-48-minute Win Share rate), BPM, RPM and PIPM from the previous season assigned to them. For each team, a total Win Share number is summed from the various players’ WS/48 and minutes. Each team also has the minutes-weighted average BPM, RPM and PIPM calculated.

The Win Share sum for each team is the win projection. Easy. The impact numbers are more difficult, as they are represented as a point differential (margin of victory). Since there are five players on the court at any time, the average point differential determined by each statistic is multiplied by five, then a Pythagorean wins calculation is used to translate that point differential to a win-loss record.

One last adjustment is made, which is to make sure the league average win total for each projection is 41 wins. For each statistic, we just subtract (or add) the excess (or missing) average win total from every team. So, for example, the average win total across the league using Win Shares came to 43.2 wins — so every team had 2.2 wins removed from their projected total to bring the average down to 41 wins.

Predicting The Raptors

So, we’ll walk through the Raptors as an example. The following chart shows the input values — the players on the roster, the minutes totals from last year, and each player’s WS/48, BPM, RPM and PIPM. Note that here is one of the exceptions I tried so hard to avoid — I’ve used 2016-17 numbers for Kawhi instead of 2017-18. PIPM has a multi-year version, so I’ve used that instead of the one-year version for either 2016-17 or 2017-18.

Now, this is not entirely fair, as I try to keep my hands off any such assumptions, but Kawhi is such an extreme case that I felt I had to intervene. I did the same for Gordon Hayward, who also barely played last season. Same goes for Jeremy Lin, Jon Leuer, Mike Conley and Patrick Beverley. I had to cut the extreme cases off somewhere or this would take forever and I’d feel like I muddied the process too much, so I drew the line at having missed at least 70 games. Next year I have plans to maybe use two or three season minutes-weighted averages for all values to cut out this meddling, but that’s next year.

In any case, here is that chart, with the 2017-18 minutes and production inputs, as well as the pro-rated 2018-19 expected minutes, and the contribution of each individual to the team level Win Shares, BPM, RPM and PIPM numbers.

With those inputs, the team total wins and point differentials come to:

With the ratio described above leaving WS the most important predictor, then PIPM, followed by BPM and RPM, the league wide average win totals come out to:

That’s right, the model is predicting the Raptors to post the single best record in the NBA next season, though they do come a bit shy of 60 wins. Two of the four statistics agree on that, with RPM and PIPM thinking Golden State edges the Raptors out for the top spot.

Around the league, some interesting results — LeBron misses the playoffs (I wouldn’t be betting any real money on that one, personally), as does his old team (though a Washington implosion could fix that), and the Spurs end up in the middle of the Western Conference playoff bracket ahead of the star-studded OKC team. Portland slides out of the playoffs despite their expensive roster. The bottom half of the Eastern Conference playoff bracket looks like a mess of a lot of very mediocre teams, while the top four seem to have created some serious separation from that mess. As is tradition, two teams in the West project to miss the playoffs with a better record than the 8th seed in the East.

So, what do you think? Is this too optimistic? Perhaps the assumption that Kawhi returns to form is too optimistic in the first place. Or do you agree that the Raptors could be looking at the top seed throughout the playoffs if all goes well?

If you have any specific situational questions (like, what if Kawhi misses the season, or OG Anunoby and Pascal Siakam improve a lot, or Serge Ibaka sees a big minutes cut, or Jonas Valanciunas’ minutes go up, or questions about other teams), feel free to toss them my way and I’ll try to plug in those scenarios and come up with how much things might change.

And keep in mind this is all for fun. Once the ball goes up in October all bets are off. Still, never hurts to see the Raptors projecting so well.

Make sure to check out Basketball-Reference, ESPN and Jacob Goldstein’s twitter for the sources for these stats. All stats and rosters were pulled as of August 6th, 2018.