Free Agents’ & their Individual Offensive Efficinency by Henry Yeh

Our dearest contributor Henry, while at the Utah Summer League did some behind the scenes analysis as the Free Agency market has been booming.

Here is his article that he recently posted up on his own blog:

http://basketball-datascience.com/2017/07/05/free-agents/

With the July moratorium (7/6, 11:00am CT) looming in as teams can begin officially signing players and making trades, I think it’s interesting to take a look into the free agents’ Individual Offensive Efficiency (IOE). I have attached the players’ games played, minutes, and the traditional stats.

 

(Note: UFA = Unrestricted Free Agent; RFA= Restricted Free Agent; *Player Option; **Team Option; ***Early Termination Option; Festus Ezeli’s stats are from last season with the 2015-16 Golden State Warriors )

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The formula for the IOE is Points Generated (PGEN) divided by Net Possession Terminated (NPT). The formulas are as below:

PGEN = Pts + ((Assisted Points / Player’s Tot Ast) * ( Ast + 2nd Ast + FTAst ))

NPT = FGA + Ast + 2ndAst + FTAst + ( 0.44 x FTA) – ORB + TO

whereas the PGEN measures the amount of points a player produces each time he  terminates a scoring possession while the NPT takes into account of all the possible actions that end a possession ( 1) field goal attempts, 2) assist of any type, 3) shooting fouls, 4) offensive rebounds, and 5) turnovers ). We use the parameter 0.44 for free throws because a player can shoot anywhere between one to three free throws. We subtract offensive rebounds because an offensive rebound extends a possession (it does not terminate the possession). Click here more details about these formulas.

Now we will have a new list ranked by the IOE. Cristiano Felicio has the highest IOE while Jordan Hill has the lowest (because he did not record an assist). Note that this list is not accurate to judge a player’s overall offensive ability since a lot of these free agents are fringe players, meaning they played very few games and minutes, which won’t garner any statistical significance. This is manifested by the fact that eight out of the top ten players are reserves and aside from Andre Iguodala, none of the other nine players have averaged more than 20.1 minutes per game. However, this list is still interesting to look at as it shows where some of the rotation players stand.

 

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— J.H. Yeh

(all tables are created by me, and all sources of stats are courtesy of NBA.com as of July 2, 2017)

 

 

Win Probability

Introduction

With the end of the NBA Finals series last night, the national leagues that I mainly cover are over. With that I wanted to post a special piece that pays homage as well as explains what I had been focusing on since February and the launch of Advance Pro Basketball. To us analytics guys, aside from the statistics, and myriad of other qualitative and quantitative evidence that allows us to delve deeper into how a team wins and how players contribute to the “W”s; win probability calculations, lead tracking and changes as well as elements that relate to how win probabilities shift from one end to the other have been a focus for me.

What is win probability?

Simply put, win probability is a tool that helps calculate a team’s chance of winning a game at any given time, based in historical performance of similar teams in the same instance or situation.

Originally developed by Bill James for baseball and the “moneyball” notion it later on spread to other sports including basketball.

The current research which I too take part in involves the accuracy measurement of win probability estimations. That is, if an independent tool say estimates a 20% win probability because 20% of teams previously won in that situation/instance, do future teams win at the same 20% ratio?

The hard part is estimating from hidden data that makes use of testing/simulation tools like cross-validation.

While most prediction models involve analyzing frequency of past events, other models use Bayesian processing.

Since February along with other anayltics experts have been looking closer at models including a measure of teams’ strength coming into the game, while others assume every team is average. Adding in the strength factor estimates increases the number of probable states, and hence decreases an estimation power while possibly increasing its accuracy.

Recent research and Basketball win probability

Before discussing in depth on how current research is going, I wanted to give a bit more insight on the scope of work that has been done in recent times.

Every year at Sloan Sports Analytics Conference, we see more and more research papers that cover a wide range of topics. In 2012 one research paper that delved into the matter gave lots of weight on win probability calculations, here is a video to the paper explanation during the conference:

Win Probability Added

Within the realm of win probability calculations we also tend to look at players contributions individually and it is at this point that attempting to measure a players probable contributions before they take place are tricky and while most coaches consider this as delivering over-use of data and making their job obsolete. However to the contrary as analytics experts in basketball we can only estimate and calculate as events take place and while live solutions currently do exist taking on the decision making process of acting as a coach is not what we aim to do.

I should be clear that win probability added calculations are not the same as win shares. While both might indicate to a players overall contribution to the team stakes in winning games the win share attribute of say 0 pointing to no contribution whereas with win probability 0 points to the average.

Current Resarch and best field application tools

With regards to the work I do while I would love to share proprietary information my client relations do not allow me to do so however several key peers that do win probability calculations have information up on their websites.

Michael Beuoy by far out of most of our peers has the best site as well as probability calculator which can be reached through:

Live NBA Win Probability Game Box Scores

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NBA Win Probability Calculator

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Misconceptions about win probability

With regards to this section of my post, I want to be very open and clear that there are many elements that go into calculating and graphing win probabilities and outcomes that indicate results are not definite. The countability of away games to home games, star players vs. high producing bench players, lead changes during games and possessions as well as pace are all factors that contribute to win probabilities. Taking away any or all of the above or omitting data does create probability miscalculations in my perspective.

Where will win probability and basketball analytics lead us?

Given the scope of current research and the progress that we are heading in the aim is to be able to have win probability calculations more commonly used in Europe as well as in Turkey soon enough. I should point out that at present given that with held data by local basketball government bodies across Europe might hinder the progress as play by play data as well as lead change data are crucial, furthermore the fact that historical data as mentioned earlier are also very important to take into consideration.

To close off here is a graph and table depicting the final NBA game of the season between GSW – CAVS:

Screen Shot 2016-06-20 at 13.52.22 Screen Shot 2016-06-20 at 13.52.00

NBA FİNALS: GAME 7 THE FINAL GAME: CAVS 4 – 3 GSW (LEBRON OVERCOMES ADVERSITY!)

With the 7th and final game of the NBA Finals series being played out last night, we saw an amazing game 7 “that was one for the books” as LeBron stated after the final buzzer was heard and the CAVS lifted up the Larry O’Brian Trophy and LeBron was crowned the Finals MVP as he lifted up the Bill Russell Finals MVP Trophy. After +50 years Cleveland finally got a major sporting championship from one of its teams ending the draught. The CAVS also became the first ever NBA Champions rallying back from a 3-1 deficit to win the title.

While Golden State did their best to over come their own adversity issues, Curry, Thompson and Green combined were not enough to prevent LeBron, Irving, Love and T.Thompson from winning. To me a sad note was the missing presence of Andrew Bogut whom had a good season and was injured during the finals series. Harrison Barnes was also extremely bad on both ends of the floor and was not able to contribute enough when needed.

While GSW did manage to get the illusive 73 win season, I firmly believe that it was a matter of bio-metrical fatigue and mental tiredness that took its tool on the Warriors in the playoffs. Cleveland having swept both their first and second round playoff opponents had more than enough time to rest and while their path to the playoffs were much different than the Warriors in part I strongly feel that having had more chance to rest and prepare mentally gave them the edge in the finals to push themselves on the court much more than Curry and Thompson did.

Below, a highlight of the final game, analytics charts detailing the game and a win probability graph that shows the game can all be found:

Highlights of the game:

Performance of Starters:

Screen Shot 2016-06-20 at 08.50.39

Performance of Bench players:

Screen Shot 2016-06-20 at 08.50.47

General team stats of the 7th game:

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Team Leaders of the game:

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The Score breakdown of the 7th game:

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Hustle stats of the 7th game:

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Win probability graph during the seventh game:

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NBA FİNALS: GAME 6: CAVS 3 – 3 GSW (The last stand at Oracle Arena!)

With last nights game 6 being played out in legendary fashion, Cleveland have tied up the series at 3 a piece and take have become the 3rd team to tie the NBA Finals Series coming back from 3-1 deficit. The CAVS did not give GSW any chance to take the lead and played an astonishingly well defended game.

With this post I am sharing highlights of the game, an analytical review of both teams as well as my win probability graphs and probabilities for the final game of the NBA.

 

Highlights of the game:

 

Performance of Starters:

Screen Shot 2016-06-17 at 09.17.06

Performance of Bench players:

Screen Shot 2016-06-17 at 09.17.12

General team stats of the 5th game:

Screen Shot 2016-06-17 at 09.16.42

Team Leaders of the game:

Screen Shot 2016-06-17 at 09.16.52

The Score breakdown of the 5th game:

Screen Shot 2016-06-17 at 09.16.30

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Hustle stats of the 6th game:

Screen Shot 2016-06-17 at 09.17.40

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Win probability graph during the sixth game:

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Seventh Game & NBA Finals Series Win Probability:

Screen Shot 2016-06-17 at 08.58.30

NBA FİNALS: GAME 5: CAVS 2 – 3 GSW (LEBRON & KYRIE IN ACTION)

With last nights NBA Finals game 5 Cleveland finally responded to Golden State with the much needed urgency and hard defense against the offensive prowess led by Curry.

The critical notes of the game: Andrew Bogut going down in the 3rd quarter with 10:30 to play and interestingly LeBron and Kyrie both scoring 40+ points as teammates in a playoff games as they make NBA history.

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With this post you will find a highlight of the game (courtesy of the NBA), a breakdown of the analytics with respect to game charts and for the first time a win probability look at both game 6 and (if necessary) game 7 as well as the updated NBA Finals series win probability.

Highlights of the game:

Performance of Starters:

Screen Shot 2016-06-14 at 09.18.08

Performance of Bench players:

Screen Shot 2016-06-14 at 09.18.14

General team stats of the 5th game:

Screen Shot 2016-06-14 at 09.17.50

Team Leaders of the game:

Screen Shot 2016-06-14 at 09.17.57

The Score breakdown of the 5th game:

Screen Shot 2016-06-14 at 09.17.37

Screen Shot 2016-06-14 at 09.18.03

Hustle stats of the 5th game:

Screen Shot 2016-06-14 at 09.41.45

Screen Shot 2016-06-14 at 09.41.57

Win probability graph during the fifth game:

Screen Shot 2016-06-14 at 09.18.47

NBA Finals Series Win Probability after the fifth game:

Screen Shot 2016-06-14 at 09.20.07

Sixth & (if necessary) Seventh Game Win Probability:

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