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1. ## NBA possession formula / NBA totals

I found this formula on nbastuffer.com and it is the type of thing I was looking for as a jump off point.

More Specific Possession Formula=0.5 * ((Field Goal Attempts + 0.4 * Free Throw Attempts - 1.07 * (Offensive Rebounds / (Offensive Rebounds + Opponent Defensive Rebounds)) * (Field Goal Attempts - FG) + Turnovers) + (Opponent Field Goal Attempts + 0.4 * Opponent Free Throw Attempts - 1.07 * (Opponent Offensive Rebounds / (Opponent Offensive Rebounds + Defensive Rebounds)) * (Opponent Field Goal Attempts - Opponent FG) + Opponent Turnovers)).
This formula estimates possessions based on both the team's statistics and their opponent's statistics, then averages them to provide a more stable estimate.

A few questions. I have this formula, how can I structure it in order to help with daily games? Also, what would be next step in terms of the data and everything.

I would appreciate any help.

2. This gives you one team's "pace rating". If you know both team's pace rating, and projected offensive and defensive efficiencies, you can set both a side and total for a game.

3. Originally Posted by Justin7
This gives you one team's "pace rating". If you know both team's pace rating, and projected offensive and defensive efficiencies, you can set both a side and total for a game.
"pace rating" is not the same as actually watching the pace. get the league pass and rather than only looking at stats you can confirm this formula by visualizing the pace and game tempo. I have seen this formula used before and its pretty effective but it can easily deceive you

4. I find that the more even the teams are the more chance that the game gets played at the expected tempo. The books odds are as good as any in indicating the close games.

5. I have been betting for a long time and watch lots of games. I am just trying to supplement with some stats.

6. Originally Posted by Justin7
This gives you one team's "pace rating". If you know both team's pace rating, and projected offensive and defensive efficiencies, you can set both a side and total for a game.
How exactly do you go about setting the side and total with pace ratings, projected offensive, and projected defensive efficiencies? Is there a specific formula?

7. Originally Posted by dvsbmx
How exactly do you go about setting the side and total with pace ratings, projected offensive, and projected defensive efficiencies? Is there a specific formula?
pace rating will be possessions per game, and efficiency is points per possession

from there its basic math

8. im beginning to think that the formula is merely constructed from mining past years with prior expectation of finding a formula, and would contend if you bet every game with any advantage on the side and total you will be 50/50

9. good luck guys

10. Originally Posted by uva3021
pace rating will be possessions per game, and efficiency is points per possession

from there its basic math
Thanks UVA. After I wrote the question and was falling asleep in bed thinking about it I kind of figured it out.

Originally Posted by uva3021
im beginning to think that the formula is merely constructed from mining past years with prior expectation of finding a formula, and would contend if you bet every game with any advantage on the side and total you will be 50/50
Would this still hold true if you projected efficiency using starting line ups possibly or weighting players minutes somehow?

11. the APBR forums are a good source for dissecting this stuff in tiny minutia

12. Someone please correct me if I'm wrong but here is how I'm trying to figure out expected points.

Expected points= Offensive Efficiency/100*Pace

If offensive efficiency is points per 100 possessions then you need to divide by 100 to find points per possession then multiply by pace to find the expected score. Where I'm getting stuck (if what I have shown is actually correct) is how to incorporate defensive efficiency. I know that DEFF is points allowed per 100 possessions but can't figure how to incorporate that into the overall picture of setting the line and total. My only idea would be to compute the teams and opposition's expected points allowed and take the difference although I still don't know how to incorporate that into a side or total.

As uva3021 stated, this would probably be 50/50 if an advantage were found. My objective right now is to learn more than anything else, if the winners come I'll take them but for right now I just need more knowledge.

Anyone that can give me some solid answers/advice/anything else will be thrown some points.

13. if you think of the offensive and defensive efficiency as percentage of points scored per each possession, then to incorporate an opponents efficiency factor into a team's expectation you would multiply the teams respective efficiency numbers by the average pace

For example

Team A: Pace 95.1
Team B: Pace 90.1

Expected Pace = (95.1+90.1)/2 = 92.6

Team A: OE = 1.01
Team B: DE = .91

Expected Team A points = 1.01*.91*92.6 = 85.11

Team A: DE = .85
Team B: OE = .99

Expected Team B points = .85*.99*.92.6 = 77.92

Final score

Team A: 85.11

Team B: 77.92

Here is a spreadsheet I made about 3-4 years ago, one for NBA one for college with the formulas already filled in, you just need to enter the pace and efficiency numbers in the appropriate cells (for NBA I use 82games.com for stats, NCAAB I use kenpom and bbstate)

THe files are in excel. Make sure you DO NOT fill down the formulas for they are inconsistent from one cell to the next. Each cell corresponds to its preceding crossover, so if you need to add cells do it manually. I have pre-filled a ton though so it should be plenty to get you started
Points Awarded:
 dvsbmx gave uva3021 5 SBR Point(s) for this post.

14. Originally Posted by uva3021
For example

Team A: Pace 95.1
Team B: Pace 90.1

Expected Pace = (95.1+90.1)/2 = 92.6

Team A: OE = 1.01
Team B: DE = .91

Expected Team A points = 1.01*.91*92.6 = 85.11
This is just horrible. It is nothing more but pseudo-math. You simply cannot make any estimates without knowing league averages. And once you know, you will use different formulas that take those averages into account.
Points Awarded:
 Justin7 gave Data 2 SBR Point(s) for this post. Justin7 gave Data 2 SBR Point(s) for this post. CFA gave Data 2 SBR Point(s) for this post.

15. Originally Posted by Data

This is just horrible. It is nothing more but pseudo-math. You simply cannot make any estimates without knowing league averages. And once you know, you will use different formulas that take those averages into account.
Here are the league averages:
Code:
```Pace	OffEff	DefEff
97.5	102.5	102.4```
What formulas would be used? I remember watching Justin7's NFL prop example video where he talks about normalizing using the league's average. Could something like Justin's example work here?

16. Originally Posted by Data
This is just horrible. It is nothing more but pseudo-math. You simply cannot make any estimates without knowing league averages. And once you know, you will use different formulas that take those averages into account.
why would league averages dictate a particular teams tendency, the average just happens to be a cumulative value of play, each team is its own variety though among a system of variation

there is nothing about the style of play of Yale that should factor into analyzing Duquesne

and straight multiplication and calculating averages is anything but what you refer to as "pseudo-math", this was just a basic example to grab an abstract of how the teams compare

17. Originally Posted by uva3021
why would league averages dictate a particular teams tendency, the average just happens to be a cumulative value of play, each team is its own variety though among a system of variation

there is nothing about the style of play of Yale that should factor into analyzing Duquesne

and straight multiplication and calculating averages is anything but what you refer to as "pseudo-math", this was just a basic example to grab an abstract of how the teams compare

I agree, simple computation yes, pseudo-math no, .....for the OP -- next step if you want the model (admittedly very simple at this point) to do more than just read back averages to you -- you have to model in some substantive reason why you theorize in this particular matchup one or more of the factors will be altered.

for example, maybe when the Lakers play easy half court oriented teams their pace is different, or their offensive efficiency is different,

so plug this difference into the formula and you have a theoretical expectation of how this game's result will differ from what the mere averages reflected in this fomula would dictate.

I didn't say it would work, I am saying it is a way to begin to think about it -- of course it would take much work and testing to arrive at something you would trust enough to lay money on!

18. You can try, posting here results

19. Good luck with averages, try medians if you really want to be a pointy-head, Data.

uva is just trying to give a few examples for the noobs...I don't see you posting anything like this.

20. Lemme do the calculations for today's games real quick and we'll see what we have.

21. Obviously I DO NOT recommend playing any totals based on these results only, but I think it would be interesting to see how this plays out for a few days and maybe see if we can throw up some filters. Who knows until we try, right?

Game 1:Indiana Pacers vs. Milwaukee Bucks
Est. Score:
IND: 95.88
MIL: 95.9
Est. Total Score: 191.78
Total Line: 195.5
Play: Under 195.5

Game 2: New Jersey Nets vs. Orlando Magic
Est. Score:
NJ: 83.3
ORL: 104.1
Est. Total Score: 187.4
Total Line: 194.5
Play: Under 194.5

Game 3: Cleveland Cavaliers vs. Philadelphia 76ers
Est Score:
CLE: 91
PHIL: 100.8
Est Total Score: 191.8
Total Line: 188
Play: Over 188

Game 4: Charlotte Bobcats vs. Detroit Pistons
Est Score:
CHA: 99.5
DET: 96.4
Est Total Score: 195.9
Total Line: 183.5
Play: Over 183.5 Note: The difference between the estimation and line is more than 10 points.

Game 5: Washington Wizards vs. New York Knicks
Est. Score:
WASH: 96.6
NY: 110.4
Est Score: 207
Total Line: 208
Play: Under 208

Game 6: Chicago Bulls vs. Boston Celtics
Est. Score:
CHI: 97.5
BOS: 105
Est Total Score: 202.5
Total Line: 195
Play: Over 195

Game 7: Miami Heat vs. N.O. Hornets
Est. Score:
MIA: 100.6
NO: 88.3
Est Total Score: 188.9
Total Line: 188
Play: Over 188

Game 8: Memphis Grizzlies vs. Phoenix Suns
Est. Score:
MEM: 108.9
PHX: 113.4
Est Total Score: 222.3
Total Line: 221
Play: Over 222.3

Game 9: Utah Jazz vs. Golden State Warriors
Est. Score:
UTA: 113.1
GSW: 115.4
Est. Total Score: 228.5
Total Line: 223.5
Play: Over 223.5

Game 10: Toronto Raptors vs. LA Lakers
Est. Score:
TOR: 102.6
LAL: 119.4
Est. Total Score: 222
Total Line: 212.5
Play: Over 212.5 Note: 9.5 Point difference between Estimation and Line

We'll see how this goes but at first I'd only put small plays on games 4 and 10 since those had the greatest difference in points between the estimated score and the line. So yeah, these are today's predictions based off that basic formula. Let's see how it does and look for how we can fine tune it. (if possible)

Regardless of if it works or not, it'll definitely be a fun little project!

22. Originally Posted by uva3021
why would league averages dictate a particular teams tendency, the average just happens to be a cumulative value of play, each team is its own variety though among a system of variation
Think of what kind of teams you are looking at. Say, OE(A)=1.01 and DE(B)=.91. If the league average is .9 then you are looking at Team B which is below average defensively facing Team A which is an outstanding offensive team. The simple logic dictates that OE(A)(ab)>OE(A) while your calculations produce OE(A)(ab)
Accounting for league averages is only the first and trivial step. Next thing you want to account for is the strength of schedule.

23. Originally Posted by dvsbmx
What formulas would be used?
I don't mind helping when people are stuck but I think that doing homework is more beneficial than being spoon fed.

24. Originally Posted by Peregrine Stoop
the APBR forums are a good source for dissecting this stuff in tiny minutia

25. you would want to account for the average of the teams they have played, but the above re-conceptualization of the numbers is noted

26. Originally Posted by Raynor21
Lemme do the calculations for today's games real quick and we'll see what we have.
yea agree it is a fun project. i'm a bit busy this weekend but i'll have a go at getting some strength of schedule calculations together for the teams as soon as a got some time, maybe by monday. we can add to it as we go along... or you can just tell me that i'm doing it wrong.

a quick follow on question from some points raised already, would you take in to account what happened last few times these teams met? i mean if there is any tendencies or not. i'm not especially knowledgeable with hoops but for soccer i know that teams change their style depending on the opposition. do top NBA teams adjust and play better D when they play each other? or do certain players always under perform when being guarded by a certain player? do very poor teams play harder when they play some1 they can beat or when they play LA/miami.

i used to coach BB and found some players are just not able to deal with some1 or another. obviously UK university BB and the NBA are not even close to the same thing.

anyway if no 1 has done more on this, then i will add to it - just trying to learn.

27. I just finished reading Conquering Risk this past week and there is a chapter which does a good job of going through the steps to build a totals model based on offensive/defensive efficiency ratings in the WNBA. Using the basics from that chapter, you could begin to build your own model for the NBA. You might want to check it out if you want to learn more.

A lot of other good stuff in the book as well. I have been only begun building models in the past few months but this book helped me figure out what to look for in building these models. I'm trying to find other books and info to help me structure accurate models. I know I have the math understanding down at this point, now its a matter of testing the model and figuring out all the ingredients to add to it to make it relevant. Fun stuff, in my opinion.

28. Strength Of Schedule (SOS) Formula= (2/3)* (Opponents' Average Winning Percentage)+ (1/3)*(Opponents' Opponents' Average Winning Percentage)

i found this but not sure about it. can any1 comment on this formula please.

29. Originally Posted by Flying Dutchman
Good luck with averages, try medians if you really want to be a pointy-head, Data.

uva is just trying to give a few examples for the noobs...I don't see you posting anything like this.
But his math is wrong. To derive expected pace, you need to know the league average pace. You also need to know the league average points per possession to get the expected score.

30. OK, the extremely simple calculations I did earlier netted a day record of 7-3. It's interesting to note that two of the losing plays, Game 2 and Game 10 lost to the line by .5 point and 1 point respectively.

It's also important to note that Game 10 was one of the two games where there was a huge differential between the estimation and the line, and that play missed.

Just interesting as a jumping off point. Definitely don't see it hitting 7-3 consistently.

31. Hey Raynor 21 I played around with the fromula and got very similar numbers and also went 7-3. I'm not running to Vegas with is but it's something fun to do. I got my stats off teamrankings.com and did the math by hand...I'm not the best with computers and I was wondering if there is a quicker way you got your numbers than doing it by hand

32. Hey Raynor21 I think I sent you a direct message or soemthing again showing my ineptness with computers. But what I wanted to say was I did the same simple math fromula for the o/u and got very similar results while going 7-3. Now I'm not running to Vegas with this formula but it is fun to play arouind with. I got my stats from teamrankings.com and did the math by hand...I am not the best with computers and was wondering how you did your math and if there is a quicker way then by hand

33. Originally Posted by CFA
But his math is wrong. To derive expected pace, you need to know the league average pace. You also need to know the league average points per possession to get the expected score.
You need the league average, but not the points per possession if you are already accounting for league average offensive and defensive efficiencies.

34. If you are modeling at the team level (as opposed to the player level), you might want to look into using Solver to factor in a strength of schedule component - minimize the difference between expected and actual values.

35. Originally Posted by Justin7
You need the league average, but not the points per possession if you are already accounting for league average offensive and defensive efficiencies.
If your efficiency margins are listed in points per possession (or per 100 possessions) you need to incorporate the league average points per possession within your projected score analysis.

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