Our NBA Player Projections Are Ready For 2018-19 Filed under NBA

Just in time for the NBA’s free agent bonanza (headlined by LeBron James’s The Decision: Part III), FiveThirtyEight has re-launched CARMELO, our NBA player projection system, with forecasts for 2018-19 and beyond.

CARMELO NBA Player Projections: Our probabilistic forecast of what a current NBA player’s future might look like, based on similar players throughout history. See our projections for 2018-19 and beyond »

The basics of the system are largely similar to previous years, with the backbone of CARMELO remaining an algorithm that compares current players to past ones who had statistically similar profile through the same age. For instance, Utah Jazz phenom Donovan Mitchell is similar to players such as Gilbert Arenas, Ray Allen, Stephen Curry, Ben Gordon, Victor Oladipo and O.J. Mayo through this early point in their respective careers. Some of those players (Allen, Curry) became superstars, while others (Gordon, Mayo) didn’t really pan out. The combination of those good and not-so-good outcomes gives us a probabilistic forecast for the rest of Mitchell’s career.

In a moment, I’ll describe the (relatively minor) changes to CARMELO this year, but first, a quick word about how the system did last season.

How CARMELO Performed in 2017-18

In the regular season, CARMELO performed very well, as its preseason projections were the second-most accurate of the 20 projection systems tracked by the statheads at the ABPRmetrics message board. It also correctly predicted the Warriors defeating the Cavaliers as the most likely NBA Finals matchup (not that this was a bold take, exactly).

CARMELO was also among the most accurate regular-season prediction systems in its debut year in 2015-16, although it was one of the least accurate in 2016-17. What accounted for these differences? In 2015-16 and 2017-18, CARMELO used a combination of Real Plus-Minus and Box Plus/Minus to make its forecasts, whereas in 2016-17 it solely used BPM. (CARMELO uses a mix of two-thirds RPM and one-third BPM rather than weighting them equally.) While we’ll want another two or three season to know for sure, we strongly suspect that the RPM/BPM blend outperforms BPM alone.

In addition to our preseason projections, we update our projections for teams — though not for individual players — during the season using a version of the Elo rating system. (These are our so-called “CARM-Elo” projections.) In contrast to our preseason projections, these in-season projections sometimes seemed pretty hinky last season, at least when it came to forecasting the NBA playoffs. Despite an adjustment that was meant to give extra credit to teams with more playoff experience, the system seemed to overcorrect based on how teams finished out their regular seasons — getting too bullish on the 76ers and Raptors, for example, and too bearish on the Warriors and Cavs.

One potential shortcoming is that whereas our preseason projections use depth charts and projected playing time for each team, the in-season updates do not. For instance, they had no way to account for Curry’s injury late in the regular season and subsequent return in the second round of the playoffs. One solution could be to continuously update depth charts (and CARMELO player projections) throughout the season instead of just at the start of the year. We’re examining alternatives; just know, for now, that we think the preseason CARMELO projections are pretty smart, but that our Elo-based method for updating them during the season might require some rethinking.

What’s Different In CARMELO for 2018-19

The major fix to CARMELO this season isn’t a change in how the system projects players, but rather in how it values them. Like many modern statistical systems, CARMELO uses a notion of the replacement-level player in calculating player value. A replacement-level player, in theory, is someone who is freely or cheaply available, e.g. a G League player or someone signed to a contract for the league minimum salary.

How good, or bad, are these sorts of players? They’re below-average — but it turns out they’re not that far below average. Over the past four seasons, players signed to minimum-salary contracts or two-way contracts subtract about 1.5 points per 100 possessions from a team’s scoring margin , relative to an average player — somewhat better than the -2.0 points per 100 possessions that CARMELO had assumed before. Moreover, we found that the quality of replacement-level players differs quite a bit by position. As the NBA shifts to smaller lineups, it’s relatively easy to find reasonably productive bigs on the waiver wire (see my colleague Chris Herring’s story on the sad saga of Roy Hibbert). But it’s harder to find good wings or point guards. Thus, our calculation of wins above replacement is now position-based, using the values you see in the table below.

How good are replacement-level NBA players? Based on performance of players signed to minimum or two-way contracts, 2014-15 through 2017-18 View more! Sources: ESPN.com, Basketball-Reference.com

The overall effect of these changes is, first, to boost the value of guards and wings relative to big men, and second, to increase the value of star players relative to average ones. (If you can sign a guy who’s only slightly below average for the minimum salary, you shouldn’t pay that much of a premium to grab a merely average player instead.) We think this brings CARMELO a little closer to how NBA teams actually value players.

One other small modification is that we now issue projections for American-born players who had little or no NCAA playing time, such as Anfernee Simons (who decided to forgo his college eligibility) or Michael Porter Jr. (who played only three games at Missouri because of injury). These projections use a limited set of information — namely the player’s position, height, weight, age, draft position, and their career number of NCAA minutes played. (Our regular projections for rookies, by contrast, use NCAA stats that have been adjusted for pace and opponent quality, as provided to us by ESPN’s Stats & Information Group). Last year, we’d introduced similar projections for players who played in Europe (or another international league) rather than the NCAA; this is simply an extension of that system for players who neither played in Europe nor the NCAA.

Finally, as a small token of appreciation our for people who made it through this entire story on methodology, here are our new CARMELO projections in capsule form, with a summary of 2018-19 as well as long-term projections for the more than 600 players in our system. Have fun browsing through the CARMELOs and enjoying the drama of the NBA offseason.

2018-19 CARMELO projections, in one chart View more! * Upside is a measure of WAR where values lower than zero are treated as zero instead. That is, it doesn’t punish a player for being below replacement level, but does reward him for being above replacement level. The 7-year calculation reflects the 2018-19 through 2024-25 seasons.