Games where GSW has rest advantage (GSW rested; opp. tired):
– 10/27 vs. Clippers
– 11/18 @ Clippers
– 11/25 vs. Nets
– 12/23 vs. Pacers
– 1/2 vs. 76ers
– 1/4 vs. Grizzlies
– 1/7 vs. Heat
– 1/9 @ Pistons
– 1/28 vs. Jazz
– 2/10 @ Bucks
– 2/25 vs. Hornets
– 3/10 vs. Blazers
– 3/30 @ Spurs
– 4/1 @ Grizzlies
Games where GSW has rest disadvantage (GSW tired; opp. rested):
– 11/23 @ Spurs
– 12/6 vs. Wolves
– 1/5 vs. Kings
– 1/10 @ Pacers
– 1/23 vs. Bulls
– 1/29 vs. OKC
– 2/6 @ Lakers
– 3/4 @ Knicks
– 3/20 vs. Bucks
– 4/4 vs. Nuggets
by taygads
5 Comments
Wait it’s only 80 games now??
FYI, for those wondering, [Positive Residual](https://positiveresidual.com/shiny/nba/#) determines SOS as follows:
> **Strength of Schedule (SOS)**
>
> An estimate of how difficult a game or a slate of games is. At its core, SOS measures an opponent’s win probability against average league competition, given a set of scheduling conditions. When multiple contests are involved, it takes the average of all the games. Higher numbers signal tougher tests.
>
> Conventionally, SOS reflects opponent quality, which is often defined by win percentage, net rating, or points above or below average. But, beyond team strength, other factors can also influence the likelihood of victory. Since this app focuses on regular-season calendars, it makes sense to incorporate schedule-related variables and have a broader definition of SOS.
>
> Ultimately, I use a logistic regression model that is trained on regular-season games from 2001-02 through 2018-19. It estimates the probability of a home-team win using the following predictors:
>
> – Home team strength;
> – Away team strength;
> – Whether the home team is rested;
> – Whether the away team is rested; and
> – Altitude at which the home team plays.
>
> Other features, including distance traveled and time-zone changes, were also tested, but they did not contribute to improved performance. The upshot was a model similar to what Nick Restifo had put forward, with the following differences:
>
> – Whereas Restifo used normalized efficiency differential as a measurement of team quality, I use Vegas-informed Estimate of Team Strength (VETS).
> – Whereas Restifo used teams’ normalized efficiency differentials at the end of the season, I use teams’ VETS ratings heading into each game.
>
> Notwithstanding these differences, overall model performances are largely comparable.
seems better than the last couple tbh
I don’t see much here that Bob can spend the whole game complaining about so that’s a win.
Seems like our schedule isn’t as bad as last year but the opening and closing stretch of games suck ass