When trying to predict games in the playoffs, historical context is important
So I created a metric that attempts to capture playoff experience into a single number for every player over their careers
I have heard it said that players need to loose before they can win, This metric can help capture who was gone through the pain of loosing in the playoffs, and who has overcome that pain, and who has pain to come
The Index is calculated by first assigning weights to different events
.5 for playoff appearances 1 for series wins 2 for finals appearance 3 for finals win
Then normalize these values to add up to 1 for later model training
Adversity Index Formula:
\[ \text{Adversity Index} = 0.0769 \times \text{Playoff Appearances} + 0.1538 \times \text{Series Wins} + 0.3077 \times \text{Finals Appearances} + 0.4615 \times \text{Finals Wins} \]
This is what it looks like for every player in 2024.
| Player | Team | Finals Appearances | Finals Wins | Series Wins | Playoff Appearances | Adversity Index Score |
|---|---|---|---|---|---|---|
| LeBron James | LAL | 10 | 4 | 41 | 16 | 7.536200 |
| Kevin Durant | PHX | 4 | 2 | 23 | 13 | 4.537100 |
| Draymond Green | GSW | 6 | 4 | 24 | 9 | 4.383300 |
| Klay Thompson | GSW | 6 | 4 | 24 | 9 | 4.383300 |
| Stephen Curry | GSW | 6 | 4 | 24 | 9 | 4.383300 |
| Kawhi Leonard | LAC | 3 | 2 | 19 | 11 | 3.768100 |
| Al Horford | BOS | 1 | 0 | 16 | 16 | 3.691200 |
| James Harden | LAC | 1 | 0 | 15 | 14 | 3.383600 |
| Kevon Looney | GSW | 5 | 3 | 19 | 6 | 3.383600 |
| Kyle Lowry | PHI | 2 | 1 | 15 | 10 | 3.076000 |
| Chris Paul | GSW | 1 | 0 | 11 | 15 | 2.845300 |
| Tristan Thompson | CLE | 4 | 1 | 15 | 7 | 2.845300 |
| Kevin Love | MIA | 5 | 1 | 16 | 5 | 2.845300 |
| Patty Mills | MIA | 2 | 1 | 12 | 12 | 2.768400 |
| Kyrie Irving | DAL | 3 | 1 | 14 | 8 | 2.768400 |
| JaVale McGee | SAC | 3 | 3 | 14 | 8 | 2.768400 |
| Russell Westbrook | LAC | 1 | 0 | 11 | 12 | 2.614600 |
| Reggie Jackson | DEN | 2 | 1 | 13 | 8 | 2.614600 |
| Jimmy Butler | MIA | 2 | 0 | 11 | 11 | 2.537700 |
| Marcus Smart | MEM | 1 | 0 | 12 | 9 | 2.537700 |
| Jeff Green | HOU | 2 | 1 | 11 | 10 | 2.460800 |
| P.J. Tucker | LAC | 1 | 1 | 12 | 7 | 2.383900 |
| Jaylen Brown | BOS | 1 | 0 | 12 | 7 | 2.383900 |
| Jae Crowder | MIL | 2 | 0 | 10 | 10 | 2.307000 |
| DeAndre Jordan | DEN | 1 | 1 | 9 | 10 | 2.153200 |
This is what it looks like for every Team in 2024.
| Team | Finals Appearances | Finals Wins | Series Wins | Playoff Appearances | Adversity Index Score |
|---|---|---|---|---|---|
| GSW | 29 | 19 | 128 | 63 | 42.222900 |
| DEN | 15 | 14 | 87 | 54 | 28.609700 |
| MIA | 21 | 3 | 99 | 65 | 28.070900 |
| LAC | 8 | 4 | 96 | 87 | 25.762700 |
| MIL | 8 | 6 | 73 | 81 | 22.686900 |
If I had to assign classes to the range for adversity score to teams, I would say 20+ means you have overcome the pain, 10-20 means you have gone through the pain but have not overcome, and less than 10 means you have to still go through more pain.
Contenders will likely see themselves at 10+ by the time they are contending
What if we wanted to isolate “Pain” maybe we could take this score and divide or subtract based on finals wins.
Measure how close have you gotten without having been graced by a ring
So I took 2 Points off your score for every finals win and that made Pain Index
Now near the top of Pain Index we see which teams really are contenders or just were, who haven’t gotten over that hump yet.These teams might soon get a win, or failed to do so in their window