Seems to me that what the Heat did against the League is kind of irrelevant. You should be looking at what they did against the Pacers in the last 2-3 years to get a better idea of what to expect.

If you don’t have enough data points, just make some assumptions based on the games played and iterate 1,000 times. (You can use a Monte Carlo simulation) That should give you a better idea of what’s what. (Law of Large Numbers and all).

When you have that, then you can go, yeah, the numbers should look like………… and this game is a wide deviation from the average (sorry if i am a little technical here) and yada yada……………………..Your method works if you want to know what to expect at the beginning of the season. (And you should probably be careful with the league average thing because each team doesn’t play every opponent the same amount of time…you get the idea)

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You use a Monte Carlo Simulation on a situation that you can’t quantify. But since we have actual statistics, there is no need for a Monte Carlo style simulation, because we can just run a direct simulation. Which is what this is. And it is much more accurate. Because you aren’t making up numbers and variables and hoping they give you the results your looking for.

]]>Now what are we looking to do here? We are trying to understand/ predict what is likely to happen when the Heat play the Pacers during the playoffs That’s it. Not the rest of the league, not some team in Europe. Heat vs. Pacers and preferably in a Playoff situation.

What is our problem here? We don’t have enough data points to predict what can/should/will happen. That’s why we use a simulation. And I am sure that you understand that

I grant you that the Heat and Pacers have changed/grown in the past 2-3 years but those data points are the least imperfect that we have. Again, what LeBron did vs the Raptors or Blazers or any other team apart from the Pacers is kind of irrelevant.

And just for the record, when you run a simulation, you have assumptions but don’t “pick” numbers. You run a program that picks them randomly. That’s actually the whole point.

But again, you don’t have to believe me. Print the whole page and the comments, show it to a College Statistics Professor and see what she tells you.

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a simulation doesn’t know points are more important than rebounds, unless you tell it so. it can “randomly” pick a statistic that is completely irrelevant.

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and it still doesn’t make sense to use data from previous years in that small of a sample size. not at all.

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first, you can’t value the Pacers in previous seasons anything remotely close to how they are today. They have grown by leaps and bounds each of the last 2 years.

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second, your monte carlo simulation in 12 games (that’s the largest sample you can use under your suggestion) can’t take into account any context. Because your sample size is so small, any assumed/projected/random variable hinges on a completely unreliable subset.

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In 2010-11, the first year of the simulation YOU want to run, the Pacers went 37-45. Their starting lineup features ONE player from this years starting lineup. That is a completely different team. A terrible, non playoff team.

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And you think it makes sense to use that team, from 3 years ago, to help create random variables, to simulate what would happen between these teams, in a playoff series.

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You can’t honestly think this sounds more logical then using a larger set of data, from these teams, this season.

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Monte Carlo simulations are not usually used to predict the most likely outcome of an event anyway. It’s used to show all possible outcomes, and the likelihood of each outcome assuming certain decisions. So unless you have a way to know what decisions will be made, all of them, over 48 minutes, by 12 different players, 2 different coaches, and 4 referees, it makes absolutely no sense to use a Monte Carlo Simulation to predict what will happen in a basketball series when you have a large data set to use in a direct simulation.

]]>“The Simulation can’t pick numbers randomly…………”. In my email, i said that you must have assumptions, Read it closely.

“A simulation doesn’t know that points are more important than rebound unless you tell it so………..” Where in my email did I disagree with that? Beats me.

“First you can’t value the Pacers yada yada……….”. I granted you that point, I said it was imperfect.

“Monte Carlo simulation in 12 games…..” Ok, I think that’s where the disconnect is. Reread my email. You use simulation when you don’t have enough data points. If you do, you just use historical data.

The next few paragraphs are just rehashing old stuff.

“Monte Carlo simulations are not usually used to predict the most likely outcome of an event anyway”. WHAT???????

ROFL. Okay, man, you are right, whatever you say. ROFL.

]]>- http://www.palisade.com/risk/monte_carlo_simulation.asp

- And i didn’t just say, “Monte Carlo simulations are not usually used to predict the most likely outcome of an event anyway” – way to leave out the part that makes that statement relevant tho, that’s cute.

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.”It’s used to show all possible outcomes, and the likelihood of each outcome assuming certain decisions. So unless you have a way to know what decisions will be made, all of them, over 48 minutes, by 12 different players, 2 different coaches, and 4 referees, it makes absolutely no sense to use a Monte Carlo Simulation to predict what will happen in a basketball series when you have a large data set to use in a direct simulation.”

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QUOTE, “The Simulation can’t pick numbers randomly…………”. In my email, i said that you must have assumptions, Read it closely.”

QUOTE, “And just for the record, when you run a simulation, you have assumptions but don’t “pick” numbers. You run a program that picks them randomly. That’s actually the whole point.”

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Yeah you said you have assumptions, and the computer simulation picks numbers randomly.

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So you want to make assumptions, based on 6 different teams that share common players, and let the computer pick which ones matter….randomly.

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I don’t understand why you can’t see how stupid that is when you can just use a much larger subset of numbers, for these teams, with these players, this season.

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I know you keep talking about how it’s irrelevant how Miami did against Toronto, etc. But that’s not any more irrelevant than what the 2010-11 Heat did against the 2010-11 Pacers. Actually, it’s less irrelevant. Because at least half of your variables in a direct simulation represent the teams that are actually playing. Instead of using 2/3′s of your variables from teams that have nothing to do with this simulation.

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in essence, there is no variable that says “if player A makes this decision, action B will take place” because THESE ARE HUMAN BEINGS who are random variables individually, yet he thinks using a smaller data set, for multiple human beings, and making assumptions makes more sense, because basically, the team name hasn’t changed in 3 years.

]]>P.s. the pacer’s didn’t win improbably, only the author of the article is thinking that (he probably don’t even play basketball. ) the Heat didn’t lose improbably either. The heat played like garbage, and the Pacers won. The End.

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