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Category: Formulas

03/04/10

Permalink 13:21:59, Categories: Basketball, Statistics, Statisticians & Roles, Formulas  

Basketball Statistics Analysis Referred Concepts:
Basketball Statistical Formulas ~ The Prouty Rating

The Prouty is a method of rating players created by Jon Scott, based on averaging four areas: Offensive Efficiency, Total Offense, Possessions Gained and Win Rating. The various individual parts are based on a per minute basis.


...Statistics & Statisticians...

The Formula: The Prouty Rating is calculated as

[ {Points / (Field Goals Attempted * 2 + FTA) } +
{ (Points + Assists * 2 - Turnovers) / Minutes} +

{ (Rebounds + Steals + (Blocks / 2) - Personal Fouls) / Minutes} +
{ (Minutes / (TEAM TOTAL MINUTES / 5) } * Team Winning Pct} ] / 4



Excerpt of my Exposition about "Basketball Statistics Formulas Revision"
in the current Basketball Statistics Course at the eBA Clinics.

Read More at eBA CLINICS ONLINE, search "statistics formulas" in this blog and consult the eBA Basketball Statistics Analysis System.

( 'Basketball Registering Stats Referred Concepts'
is a Series of Notes edited every Thursday )

This topic is resumed in the eBA Basketball Statistics Analysis System
at the eBA Encyclopedia > eBA System.


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02/04/10

Permalink 11:22:52, Categories: Basketball, Statistics, Statisticians & Roles, Formulas  

Basketball Statistics Analysis Referred Concepts:
About the Basketball Statistics Formula 'DEVAL - Defensive Valuation'

DEVAL - Defensive Valuation, by Bradshaw (1984): On 1984 Bradshaw presents a proposal based in the following statistics:

Field Shots Made: +2 points
Field Shots Missed: -0,8 points
Free Throw Made: +1 point
Free Throw Missed: -1 point
Assist: +1 point
Defensive Rebound: +0,75 points
Offensive Rebound: +1 point
Interceptions: +0,5 points
Loose Balls recovery: +1 point
Steals: +2 points
Offensive Faults forced: +3 points
Blocks: +1 point
Jump balls gained: +1 point
Ball struggle gained: +0,5 points
Turnover: -2 points

DeVal= All the components are summed.

Read More at eBA CLINICS ONLINE, search "basketball statistics formulas" in this blog and consult the eBA Basketball Statistics Analysis System.

( 'Basketball Registering Stats Referred Concepts'
is a Series of Notes edited every Thursday )

This topic is resumed in the eBA Basketball Statistics Analysis System
at the eBA Encyclopedia > eBA System.


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11/09/09


This Week Basketball Book & eBook eBA DVICE !
Basketball on Paper ~ Rules and Tools for Performance Analysis by Dean Oliver

Basketball on Paper:
Rules and Tools
for Performance Analysis
By Dean Oliver

"... I wrote the book under the influence of award-winning books like "Guns, Germs, and Steel," "Good to Great,"
and "Ender's Game."

Now that it's 3 years after Basketball on Paper went to press, a couple years after the description here was written, and a year or more after the book got me a
job in the NBA, I wanted to add an updated description.

I wrote the book in the hopes of presenting a scientific _method_ for approaching basketball.

By "method", I mean that it doesn't present a magical all-encompassing rating for players, but rather a _structure_ for basketball as genetics provided a
structure for understanding life and biology. ...... "

"... The possession-based concept introduced early in the book allows you to evaluate strategy, the chemistry of a team, and, yes, who are better players. It doesn't matter whether it's the NBA, WNBA, college, high school, or the international game - the methods apply and I've applied them. The book focused on the NBA and WNBA because that was where the statistics were most readily available going back more than a year or two. That is changing and I have already seen foreign leagues incorporating my work into their game.

This structure does introduce formulas, nothing more complicated than the addition, subtraction, multiplication, and division that I learned through baseball cards when I was 10 years old. For coaches who want to avoid these, I actually recommend reading the last chapter first because it summarizes the conclusions of the previous chapters.

For people interested in player evaluation, the book has numerous lists of player stats, how many wins and losses they contributed, and how they did it. This includes players as far back as Bill Russell, but as recent as Kobe Bryant, Shaquille O'Neal, and Allen Iverson.

For baseball fans, I should say that Bill James' work on Win Shares in baseball paralleled mine in basketball, though we did take slightly different approaches. Bill has even said of the book, "Excellent writing. There are a lot of math guys who just rush from the numbers to the conclusion. . .they'll tell you that Shaq is a real good player but his team would win a couple more games a year if he could hit a free throw.

Dean is more than that; he's really struggling to understand the actual problem, rather than the statistical after-image of it. I learn a lot by reading him." I am happy to say that experts in statistical evaluation of baseball (like Bill), football (like Aaron Schatz and Ben Alamar of Pro Football Prospectus), and basketball are all communicating about the common goals we have for doing scientific evaluation of team sports, where analogies to business team environments and global politics abound.

I realized as I wrote the book that there were a million projects that I could do from what is in the book. Those projects have come about and grown even before I joined the Seattle Supersonics in October of 2004. Evaluating strategy has grown so much from the basics in this book. Player evaluation, especially as they move from one league to another, has evolved. And, with the experiment of the 2004-05 season, I got to show how the work significantly helps coaching a team.

That season, I joined the Sonics with a fixed roster that was universally picked for last in their division. I worked with the coaches, management, and players to provide a different perspective to their own backgrounds, one that complemented their own expertises. At the end of the season, we won our division, we upset the experts by winning our first round series, and we gave the ultimate NBA champions one of their toughest playoff battles.

I felt that we should have won the championship -- perhaps naive, but also a measure of the belief I had in not only the work in Basketball on Paper, but also in the ability of the staff and players I worked with.

Now that I do work in the NBA and apply new tricks of the trade, I can't really write another book sharing my secrets. But Basketball on Paper contains the framework, the basic insights, and a lot of the numbers for understanding a lot more about the beautiful game."
Dean Oliver


Read the complete review and an excerpt at the eBA Portal ! And ...!

You'll can also browse this book
in a Google Books 'limited preview' !
Google Basketball Books & eBooks


 • Author: Dean Oliver
 • Format: eBook & Book
 • Binding: Paperback
 • Number of Pages: 361 pages
 • Dimensions: 9 x 6 x 1.1 inches
 • Shipping Weight: 1.2 pounds
 • Publisher: Potomac Books Inc.
 • Publishing Date: November 30, 2004
 • eBook Edition: December, 2010
 • Language: English

 • Also available: In the new 2010 eBook edition

... more  Basketball on Paper ~ Rules and Tools for Performance Analysis by Dean Oliver  info at ...

Basketball on Paper ~ Rules and Tools for Performance Analysis by Dean Oliver


To see the Basketball Book eBA DVICE in Spanish:

Un Nuevo Lunes... Un Nuevo Libro Recomendado por eBA ...

Estudio Observacional de la Competición en Baloncesto por Miguel Angel Gomez Ruano
Estudio Observacional
de la Competición en Baloncesto
Por Miguel Angel Gomez Ruano











click here to read the full review !


Read more about eBA DVICES in English and Spanish
in the eBooks, Books & DVD Sections of ...

the eBA Portal, the Blogs, the eBA Store, and at the eBA Library


11/02/09

Permalink 15:01:22, Categories: Basketball, Statistics, Statisticians & Roles, Formulas  

Basketball Statistics Methods & Formulas:
Similarity Scores and The MVP IBM - IBM Watson Research Center Formula

Similarity Scores are a method of comparing basketball players to other players, with the intent of discovering who the single most similar historical player is to a certain player.
This method have been used to determine career paths and projected statistics for players. The logic behind this line of thought is simple: players often follow similar career trajectories to their most similar players, so the historical similar players' performance in years after the active player's current age should be a good predictor of that active player's future production.
Similarity scores are also used extensively in many statistical forecasting programs but is not used by the eBA Basketball Statistics Creative Analysis System.

MVP IBM - IBM Watson Research Center is one of the newest proposals in the area of the formulas to evaluate the efficiency and productivity at Basketball. It was presented by the IBM Watson Research Center together with the Technologies Commission of the NBA. This coefficient - MVPIBM - is calculated through the following equation:

MVP IBM = [ Points Made + Rebounds + Assists + Steals + Blocks -
( Field shots Made + Personal Fouls + Turnovers ) + ( Team Wins x 10 ) ] x 250 /
[ ( Points Made + Rebounds + Assists + Turnovers + Blocks -
( Field Shots made + Personal Fouls + Turnovers ) ]

This formula, with another fields formulas, are integrated in the 2nd. exposition of the Basketball Statistics Registration eBA Course: "Statistics Formulas Revision", at eBA CLINICS ONLINE.

Read More at eBA CLINICS ONLINE, search "formulas" in this blog and consult the eBA Basketball Statistics Analysis System.

This topic is resumed in the eBA Basketball Statistics Analysis System
at the eBA Encyclopedia > eBA System.


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04/03/09

Permalink 07:12:31 pm, Categories: Basketball, Statistics, Contents Online, Formulas  

Basketball Statistician's Digest:
The False Dichotomy Of Advanced Statistics

From Mouthpiece Blog By Eamonn Brennan
Download source here: ' The False Dichotomy Of Advanced Statistics '

"... Because there is not a person on the face of the planet who both understands the sophisticated analysis of basketball and thinks it’s a fad. It’s here to stay, and the sooner you’ll embrace that reality, the faster you’ll be able to gain useful insight from it. ..."

"... He said he “worried that sometimes you can get too bogged down in the numbers,” and that his strategy for learning about the game was by watching basketball day and night. He said he preferred to record games and watch them on the treadmill, which I would totally do if I didn’t hate treadmills. ..."

"... Teams are getting answers to all kind of questions. Only in the age of the spreadsheet can coaches know, beyond shooting from the hip: Which point guards turn the ball over the most when you trap the pick and roll? Which free agent power forward is the most efficient offensive rebounder from the weak side? Which players score at a vastly lower rate when going left? ..."

"... Some of the people I affectionately call "the stat geeks" have real answers to those kinds of questions. And while the coaches of teams with in-house stat geeks and custom databases might not change their routines quickly, don't you hope they spend the time learning about how the best of these statistics can be implemented? Would you really rather your team make decisions based on box-score statistics? ..."

"... For a sport like basketball, where so much of the game is slippery and infinite, stats will never replace the knowledge — not to mention happiness — you can get from merely watching the game ..."

Read more in Mouthpiece Blog


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03/27/09

Permalink 05:44:54 pm, Categories: Basketball, Statistics, Contents Online, Formulas  

Basketball Statistician's Digest:
The State of the Art: Evaluating APBRMetrics

From Basketball Prospectus By Kevin Pelton
Download source here: ' The State of the Art: Evaluating APBRMetrics '

"... Ultimately, the discussion seems to center largely on the two questions that were the focal point of my columns five years ago:

 • Can we boil a basketball player's value down to a single number?
 • What better describes a player's value: his individual statistics or plus-minus data describing his impact on the team? ..."

"... With the benefit of hindsight, the way baseball teams ignored the sabermetric revolution for years and years turned out to be a blessing in disguise, allowing a growing community to freely advance on each other's work. By the time a large number of teams got wise to the value of statistical analysis, sabermetrics was entrenched to the point where it could easily survive teams poaching some of the best talent. ..."

"... Everyone involved agrees that it would be ideal to use a dynamic method of player valuation that takes into account a player's teammates and his role. Figuring out how to turn this into reality has proven much more challenging, and this remains an area with the potential for huge advances in our understanding. ..."

"... Each system has its merits and its supporters, and certainly I think that my own WARP system is useful. However, the discussion tends to be charged and subjective when frankly I'm not sure the differences are all that noteworthy. Past a certain threshold of reasonableness, each method is probably about equally useful. The danger lies in the true believers who take any one of them to be gospel and ignore its shortcomings and blind spots. These systems are one tool to rate players, not an evaluation in and of themselves. ..."

"... Ultimately, I feel like there are areas of the game that are simply too subtle to be quantified at the individual level, like a player's positioning. If being at the right place at the right time forces an opponent to pass the ball out for a more difficult contested shot, that's virtually impossible to track. Where it does show up is at the team level, which is why I feel that even with more detailed statistics at our disposal there will still be value to looking at plus-minus numbers. ..."

Read more in Basketball Prospectus


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03/20/09

Permalink 20:46:40, Categories: Basketball, Statistics, Contents Online, Formulas  

Basketball Statistician's Digest:
The Formula That Killed Wall Street: A Cautionary Tale

From 20 Second Timeout By David Friedman
Download source here: ' "The Formula That Killed Wall Street": A Cautionary Tale for Anyone Who Places Too Much Value on Basketball Statistical Analysis '

"... Basketball statistical analysis and new video technology have already made for a good marriage in terms of helping teams to more easily produce accurate scouting reports depicting the tendencies, strengths and weaknesses of players and teams. It is now possible to quickly break down game film (or, to be precise, game DVD) and catalog what happened on every pick and roll play, every out of bounds play, every postup and so forth. Most if not all teams are already applying some form of statistical analysis to the tendencies that emerge from game footage and this obviously represents a quantum leap forward in terms of game planning. ..."

"... The problem is when some people invent certain formulas in which they add up some numbers, multiply other numbers by certain factors, subtract some other numbers and then produce a final number that supposedly "rates" a player's overall performance. ..."

"... This is very analogous to the current situation with basketball statistical analysis; too many of its practitioners "see only the numbers before them and forget about the concrete reality the figures are supposed to represent." They do not have the wisdom or humility to admit that their simple, pretty formulas represent just one, limited interpretation of raw data. ..."

"... Basketball statistical analysts do not yet have all of the necessary data to completely "model" the sport, nor do they fully understand how to use the data that they have. Trying to produce such a model is certainly a worthy task--but I just wish that the people who are working toward this goal would stop declaring "Mission Accomplished!" when the reality is that they are in the beginning or intermediate stages of that mission. ..."

Read more in 20 Second Timeout


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02/27/09

Permalink 18:20:17, Categories: Basketball, Statistics, Contents Online, Formulas  

Basketball Statistician's Digest:
Examining Defense ~ The Defensive Four Factors

From Basketball Geek By Ryan J. Parker
Download source here: ' Examining Defense ~ Defensive Four Factors '

"... The real goal in all of this is to get an idea of how coaching and player ability affect overall defensive efficiency and the individual stats that are collected. Because of this, I will be strictly focusing on each team’s most used lineup. ..."

"... One important assumption in all of this work is the notion that a team’s goal is to minimize their defensive efficiency. A team’s true goal is to maximize their net efficiency (offensive efficiency - defensive efficiency), but we will not be focusing on this for now. ..."

"... By most used, I mean the lineup that was on the court for the most defensive possessions. To make things a little easier, I have chosen to estimate possessions instead of count them. I hope this does not prove to be a mistake, but for the interested, I used the “simple” formula for estimating possessions as defined in ..."

"... Shooting, by no surprise, has been shown to be the most important factor. This factor is often analyzed using effective FG%. I, however, am biased against effective FG%. It is not actually a percentage, and I find it to have no useful mathematical properties (that I’m aware of, at least). Thus I feel compelled to use a shooting statistic that I can make more sense out of. ..."

"... The results from this sample show that, with respect to defensive efficiency, shooting is worth 56%, turnovers 18%, rebounding 7%, and free throws 18%. It’s certainly worth gathering a larger sample to truly put some sort of importance weight on each factor, but for this sample it is at least worth looking at. ..."

Read more in Basketball Geek


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02/06/09

Permalink 18:12:40, Categories: Basketball, Statistics, Contents Online, Formulas  

Basketball Statistician's Digest:
How much do turnovers hurt? This much...

From A Sea of Blue
Download source here: ' How much do turnovers hurt? This much... '

"... We hear all the talk about how turnovers are hurting this team, and anyone who has a whit of basketball IQ knows that it's true. But sometimes, it's hard to figure out just how much they are hurting, and if all turnovers are created equal. ..."

"... Obviously, these are troubling statistics. We are giving the opponent an opportunity to score an average of 21.8 points per game plus surrendering the opportunity to score 25 for ourselves. That amounts to 46.8 points per game, and is more than the averages of Jodie Meeks and Patrick Patterson put together. ..."

"... Just look at how much turnovers alone are contributing to the opponent's score. If we had managed to cut turnovers by 25%, that would represent a net gain of 6.25 points, plus a net loss of 5.5 points for the opposition, for a total of 12 additional points. ..."

Read more in A Sea of Blue


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12/14/08

Statistician's Digest: Correlated Gaussian Statistics Method

We can calculate the winning percentage to points scored and allowed
using the "Correlated Gaussian Method" explained at the eBA System Area.

It is a method that relates winning percentage to points scored, points allowed, the standard deviations of points scored and allowed, and the correlation between points scored ( which can be replaced with offensive rating ) and points allowed ( which can be replaced with defensive rating ).

"Points scored" can be replaced with "offensive rating" and "points allowed" can be replaced with "defensive rating".
eBA Stats doesn't applies this method which is fully explained at the "Journal of Basketball Studies".

This method is discussed and defined too at eBA System - The Project.

About definitions and applications you can refer to the Global Basketball Encyclopedia.


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