Sunday Stat Session: Estimating Player Trade Value

[scott]

What would Jarace Walker + Nesmith be worth to us? I.e. what would we have to give assuming Sochan is going out.

Unfortunately, for whatever reason, Walker is not in BB Index's LEBRON explorer, which my entire model is based on, so I can't calculate it. Bummer.

[mo7888]

Unfortunately, for whatever reason, Walker is not in BB Index's LEBRON explorer, which my entire model is based on, so I can't calculate it. Bummer.

Gotcha. I'm still holding out hope, although I probably shouldn't lol

[Biggems]

I really like Nikola Vucevic. What would we have to potentially trade to Chicago to acquire him. I was thinking of sending them Collins, Branham, and an unprotected first could possibly do the trick. However, I am not sure.

[scott]

After the trade deadline, I'll plug all of this year's trades into the dataset and try to further refine the model. Right now the model is a funciton of LEBRON (overall only, not offensive and defensive metrics), Age, Contract and Role (Star, Starter or Bench) to come up with a Player Score. I'll look to incorporate position (Guard, Wing, Big) and O-LEBRON and D-LEBRON as well. So, stay tuned for that.

I went ahead and plugged in Luka and Anthony Davis into the model and see what it spit out.

Unsurprisingly, Luka delivered one of the highest PlayerRating scores my model has ever spit out... but not the highest ever. You'll might be a little surprised to learn that it was actually when we traded Dejounte in 2022 that resulted in the highest PlayerScore. That was largely a function however of his contract at the time, and getting a "Star" role designation, coming off an All-Star appearance. Obviously DJM and Luka are different kind of stars, so I'm going to look to build in a "SuperStar" designation in the next iteration of the model.

The model gives Luka a 113 PlayerRating, which equates to 79.45 CompScore points. AD gets a 94 rating (which is 100% based on his age), worth 67.9 CompScore points. The 12 point difference is roughly worth 1 lightly protected FRP in my model. So... if Nico Harrison was using my model, then the trade wasn't actually that bad

Some notes on how Luka Scores a 113, AD scores a 94, and DJM scores a 132 back in 2022:

Luka has a 98th percentile LEBRON this season, AD is 99th percentile, and DJM was 97th percentile in 2022. The differences come from that in 2022, DJM's contract I graded as "Good" which is worth an additional 20 points versus the Max contract that Luka and AD are on. Likewise, Luka's age is worth 20 additional points versus AD's age.

These are good data points for improvement on my model, though this trade still seems terribly imbalanced and I'm curious how it skews the results if I include it in the dataset.

[poopbox]

No model will account for a star being a perpetually out of shape fat ass people in the organization don't feel comfortable giving over 300 million dollars to tbh. Real life isn't NBA2k. The basketball part of the Luka trade makes no sense but the business and human part of the trade makes perfect sense.

[scott]

I'm going through the process of updating my model for all of the moves that were made at the deadline for two reasons:

1) Compare the actual CompScore's versus what the model predicted
2) Update the model with the new data in time for the offseason to have a more refined prediction tool for the next trade window

With that said, just wanted to give a quick update on what my model predicted versus what actually happened on a few trades:

Player	Predicted CompScore	Actual CompScore
De'Aaron Fox	69.1	77
Luka Doncic	79.45	65
Zach Lavine	21.8	20

Note on Luka's CompScore: my model didn't previously have a way to account for receiving a SuperStar in return, so the Mavs getting AD only counts as a "High Performing Vet". This alone may explain the gap in that CompScore versus prediction. I'll adjust for this in the next iteration of my model.

At least for these 3 trades, I'd say my model was pretty close! The Spurs actually ended up paying slightly more than my model predicted for Fox, though on the surface it looks like we got a "better deal" than what we could have expected. That's because I think most people value the ATL picks and the 25 Spurs FRP at a premium, whereas my model values all Unprotected FRPs the same. We can inherently understand that an unprotected 25 Spurs FRP is worth more than a 2029 Spurs FRP, but the model isn't built to allow for differences in FRPs.

[rjv]

I'm going through the process of updating my model for all of the moves that were made at the deadline for two reasons:

1) Compare the actual CompScore's versus what the model predicted
2) Update the model with the new data in time for the offseason to have a more refined prediction tool for the next trade window

With that said, just wanted to give a quick update on what my model predicted versus what actually happened on a few trades:

Player	Predicted CompScore	Actual CompScore
De'Aaron Fox	69.1	77
Luka Doncic	79.45	65
Zach Lavine	21.8	20

Note on Luka's CompScore: my model didn't previously have a way to account for receiving a SuperStar in return, so the Mavs getting AD only counts as a "High Performing Vet". This alone may explain the game in that CompScore versus prediction. I'll adjust for this in the next iteration of my model.

At least for these 3 trades, I'd say my model was pretty close! The Spurs actually ended up paying slightly more than my model predicted for Fox, though on the surface it looks like we got a "better deal" than what we could have expected. That's because I think most people value the ATL picks and the 25 Spurs FRP at a premium, whereas my model values all Unprotected FRPs the same. We can inherently understand that an unprotected 25 Spurs FRP is worth more than a 2029 Spurs FRP, but the model isn't built to allow for differences in FRPs.

so if a team had boston's unprotected pick for this summer and another had the wizard's unprotected pick, the model would assign them the same value?

[scott]

so if a team had boston's unprotected pick for this summer and another had the wizard's unprotected pick, the model would assign them the same value?

The correct. There is just a bucket for "unprotected FRP" and they all get assigned the same value. This makes logical sense for far out picks, in my opinion, but obviously where there is a high degree of certainty (we know WAS will almost certainly be Top 5, we know BOS will almost certainly be bottom 5), they count the same and that is a flaw. With that said, I can cheat the model in some ways. For example, if BOS traded their FRP this year, I could just mark that as a "highly protected FRP" (which has fewer value points assigned to it) instead of an "Unprotected FRP". So... there are ways around it.