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Being that I mainly bet totals the next two categories are right up my ally. First, across the betting markets we are seeing totals being set on at an all-time high. As a result, four of the five teams have an offensive efficiency that ranks in the top 10 in the league. The only team that the market did not catch onto was the Orland Magic. Next, three of the five teams have a defensive efficiency rating which ranks 24th or worse in the league.
The biggest surprise of the season as a previously stated are the Phoenix Suns and we can see why! Suns are one of four teams that rank in top 10 in both offensive and defensive efficiency. In addition, the betting markets continue to set the game total for the Suns too high.
As a result, the Suns a perfect to the under averaging Furthermore, the glaring number that stands out in the Brooklyn Nets contests average points per game. Despite the high total average, the Nets are to the Under. If the markets continue to set the total high on the Nets, I can see the Nets matchups falling below the total.
Aside from the Suns being the most surprising team, the Hawks are next on the list. Furthermore, the Hawks are getting off to great starts. Lastly, there are three teams who play well in the 2nd half of games by average margin of cover. The biggest surprise on this list are the Pelicans. Currently, the Pelicans sit at on the season and a large part is due to their defense and their 2nd half play.
Due to the pandemic the world is facing the scheduling is significantly different in the NBA season. For example, we will see two teams face each other in consecutive nights or twice in three nights. So far this season we have seen this happen 10 times. As a result, five teams have swept both games, five have split the series.
This will be something to monitor going forward. Furthermore, because it is early on in the season there is not enough data to find an edge. Happy Saturday my friends! Connect with us. Schedule Factors Due to the pandemic the world is facing the scheduling is significantly different in the NBA season. Related Topics featured.
Obviously better teams win more often and vice-versa. For example, the teams who post top records who also have recent championships are far more likely than others to actually be as good as their records indicate. This is pure bayesian inference. While there may be unrelated empirical reasons for this, I think this stat may actually confirm the underlying concept. At least enough that I will talk shit if I win again. To that end, the first round is going pretty well for me so far.
Like last year, the experts are mostly in agreement. While there is a fair amount of variation in the series length predictions, there are only two matchups that had any dissent as to the likely winner: the 6 actual stat geeks split in favor of the Lakers over the Nuggets, and between the Clippers and the Grizzlies. As it happens, I have both Los Angeles teams yes, I am from Homer , as does Matthew Stahlhut though my having the Lakers in 5 instead of 7 gives me a slight edge for the moment.
No one has gained any points on anyone else yet, but here is my rough account of possible scenarios:. Making picks this year was a bit harder than in years past. At one point I seriously considered picking Dallas against OKC in part for strategic purposes , before reason got the better of me. Thus, if I had to pick which of these teams is more likely to win the championship, I might take Dallas or at least it would be a closer call. But why on earth would I think Dallas might be any good in the first place?
The normal methods for predicting playoff performance both my own and others are particularly ill-suited for the peculiar circumstances of this season:. The thing that Henry Abbott most highlighted from my Smackdown picks which he quoted at least 3 times in 3 different places was my little piece of dicta about the Spurs:. It currently puts San Antonio above 50 percent. Not just against Utah, but against the field. Not saying I believe it, but there you go. But I can explain why it loves the Spurs so much: it weights championship pedigree very strongly, and the Spurs this year are the only team near the top that has any.
Following this method, you would correctly pick the eventual NBA Champion in So perhaps the better question to ask is: How much do you gain by including the championship test in step 1? The answer is: a lot. So the 5-by-5 model almost doubles your hit rate. While there may still be room to beef up the complexity of your predictive model such as advanced stats, situational simulations, etc , your gains will be comparatively marginal at best.
In fairness, I should note that the 5-by-5 model has had a bit of a rough patch recently—but, in its defense, so has every other model. The NBA has had some wacky results recently, but there is no indication that stats have supplanted history. Indeed, if you break the historical record into groups of more-predictable and less-predictable seasons, the 5-by-5 model trumps pure statistical models in all of them. My experience was much different than in Last year, I went into this thing barely knowing that other people were into the same things I was.
For the most part, I used Twitter as sort of my de facto notebook for the conference. Part 2 covers that interview and all of Saturday. From the pregame tweets, John Hollinger observed that 28 NBA teams sent representatives that we know of this year. The conference started with some bland opening remarks by Dean David Schmittlein.
Tangent: I feel like political-speak thank everybody and say nothing seems to get more and more widespread every year. I blame it on fear of the internet. If that person had been more important like, if I could remember his name to slam him , I doubt he would have made that joke, or any other joke. He would have just thanked everyone and said nothing. And, you know, overseas expansion blah blah.
The discussion of China, however, was interesting for other reasons. Adam Silver claimed that Basketball is already more popular in China than soccer, with over million Chinese people playing it. Those numbers, if true, are pretty mind-boggling. Finally, there was a whole part about labor negotiations that was pretty well summed up by this tweet:. Opening panel summary: league execs are very smart and have done a great job with labor negotiations according to league execs.
Overall, it was a bit like any scene from the Moneyball war room, with Michael Schuckers the only pro-stats guy playing the part of Jonah Hill, but without Brad Pitt to protect him. This guy was a walking talking quote machine for the old school. On the other hand, in the discussion of concussions, I thought Burke had sort of a folksy realism that seemed pretty accurate to me.
I think his general point is right, if a bit insensitive: If we really changed hockey so much as to eliminate concussions entirely, it would be a whole different sport which he also claimed no one would watch, an assertion which is more debatable imo. At the end of the day, I think professional sports mess people up, including in the head.
Mike Milbury, presently a card-carrying member of the media, seemed to mostly embrace the alarmist media narrative, though he did raise at least one decent point about how the increase in concussions—which most people are attributing to an increase in diagnoses—may relate to recent rules changes that have sped up the game. But for all that, the part that frustrated me the most was when Michael Schuckers, the legitimate hockey statistician at the table, was finally given the opportunity to talk.
Find common ground: you both care about scoring goals, defending goals, and winning. Good face-off skill get you the puck more often in the right situations. The question is how many extra possessions you get and how valuable those possessions are? In stark constrast to the Hockey panel, the Baseball guys pretty much bent over backwards to embrace analytics as much as possible.
As I tweeted at the time:. SSAC oldjoke. Scott Boras seems to like hearing Scott Boras talk. Which is not so bad, because Scott Boras actually did seem pretty smart and well informed: Among other things, Scott Boras apparently has a secret internal analytics team. So I was excited about this one, which, unfortunately, Cuban missed. Too much noise. Stats are hard.
This panel actually had some pretty interesting discussions, but they flew by pretty fast and often followed predictable patterns, something like this:. Zarren talked about how there is practically more data these days than they know what to do with. This seems true and I think it has interesting implications. He makes the point that, with such a broad universe of trade possibilities, looking for mutually beneficial situations is the easiest way to find actionable deals.
Fair enough. Moderated by Daryl Morey. OK, can I make the obvious point that Simmons and Morey apparently accidentally switched role cards? As a result, this talk featured a lot of Simmons attacking coaches and Van Gundy defending them. There was almost nothing on, say, how to evaluate coaches , say, by analyzing how well their various decisions comported with the tenets of win maximization.
There was a lengthy and almost entirely non-analytical discussion of that all-important question of whether an NBA coach should foul or not up by 3 with little time left. There was what I thought was a funny moment when Bill Simmons was complaining about how teams seem to recycle mediocre older coaches rather than try out young, fresh talent.
So, Bill, this is for you. So due to some other pressing writing commitments, you can probably expect Part 2 to come out this Saturday Friday at the earliest. But last night I finally got around to checking the betting markets to see how the NBA Finals—and thus my chances of winning the Smackdown —were shaping up, and I was shocked by what I found.
Anyway, I tossed a few numbers around, and thought you all might find them interesting. This is actually one area where I think the normal stat geek vs. But in certain cases—particularly those with a wide range of coherent possibilities—I think the general public may even be a little too conservative about the implications of seemingly minor statistical anomalies. First, I found that most books seem to see the series as a tossup at this point.
Intuitively, this seemed off to me. Dallas needs to win 1 out of the 2 remaining games in Miami. Even at home in the playoffs, this is extremely high. But that latter number is a bit deceptive, since the playoffs are structured so that more games are played in the homes of the better teams: aside from the Finals, any series that ends in an odd number of games gives the higher-seeded team who is often much better an extra game at home. Counting the playoffs, at this point Dallas actually has a better record than Miami by one game , and they played an above-average schedule.
Not to mention, Dallas is against the Heat overall, and against them at home more on that later. So how does the market tilt so heavily to this side? Honestly, I have no idea. Many people are much more willing to dismiss seemingly incongruent market outcomes than I am. It is a well-known phenomenon in sports-betting that huge games often have the juiciest i. This is because the smart money that normally keeps the market somewhat efficient can literally start to run out. But why on earth would there be a massive, irrational rush to bet on the Heat?
I thought everyone hated them! The blue dots are the probability of Dallas winning at least one of the next two games using the same binomial formula as the function above. As I mentioned above, the Mavericks are against the Heat this season, including against them in Miami. Well, wow! I think most people—stat geek and layperson alike—would find this statistical event pretty unremarkable. Yet, sometimes these little details can be more important than they seem. But sometimes you have the result, and would like to know how it affects the probabilities of your conditions: Bayesian analysis makes this possible.
Thus, we should expect that scenarios suggesting the former will become much more likely, and scenarios suggesting the latter will become much less so. Though our case looks a little different from this, it is actually a very simple example. That sounds complicated, but it simply means that there are 4 separate options, one of which is actually correct, and each of which is equally likely. Thus, the odds of any given scenario are expressed exactly as above B is the outcome :.
The prior probability for S x is. But since these are our only options and they are all equal, that element will factor out, as follows:. Since P S x appears in both the numerator and the denominator, it cancels out, leaving our probability for each scenario as follows:. Plugging those calculations in with everything else, we get the following adjusted probabilities for each scenario:. I should note that, yes, this analysis makes some massively oversimplifying assumption—in reality, there can be gradients of truths between the various scenarios, with a variety of interactions and hidden variables, etc.
One of the things that makes Bayesian inference so powerful is that it often reveals trends and effects that are relatively insulated from incidental design decisions. Consequently, once you get used to it, you will find that you can make quick, accurate, and incredibly useful inferences and estimates in a broad range of practical contexts.
Of course, you also have to be careful: despite the flexibility Bayesian analysis provides, using it in abstract situations—like a meta-analysis of nebulous hypotheses based on very little new information—is very tricky business, requiring good logical instincts, a fair capacity for introspection, and much practice.
Though it represents a very basic idea, it is difficult to overstate its importance: Accounting for the clock can help evaluate players where standard efficiency ratings break down. Most simply, you can take the results of each shot and compare them to the expected value of a shot taken under the same amount of time-pressure.
Though speculative, I suspect that this is when players are most likely to settle for mediocre 2 point jumpers. Similarly, but a bit more difficultly, you can compare the actual curve with a derived curve to examine whether NBA players, on the whole, seem to wait too long or not long enough to pull the trigger. So, yeah, if any of you can direct me to a dataset that has what I want, please let me know.
SSAC — Benjamin Morris skepticalsports March 3, Overall, I spent more time in day 2 going to niche panels, research paper presentations and talking to people. This is a bit different from how people often think about rebounding as aggressively attacking the ball or as being able to near-psychically predict where the ball is going to come down. In theory, this might slightly decrease the chances of getting the ball when it heads in toward his previous position, but would make up for it by dramatically increasing his chances of getting the ball when it went toward the other guy.
Though a little less purely strategical, Rajiv also thought that Rodman was just incredibly good at 2. That is, he was just exceptionally good at jockeying for position. Live B. SSAC — Benjamin Morris skepticalsports March 2, Success comes with knowing more true things and fewer false things than the other guy.
But they do have images of the actual box scores, like so: Fortunately, Bird played in every game in his first two seasons, so figuring this out was just a matter of poring through 4 years of these pics: Easy peasy! In one of the Rodman posts , I wrote: Give me an academic who creates an interesting and meaningful model, and then immediately devotes their best efforts to tearing it apart!
Rather, I will point out weaknesses as I consider them, so they may not come in any kind of predictable order. If you were paying attention, of course you noticed that The Case For Dennis Rodman was really or at least concurrently about demonstrating how player valuation is much more dynamic and complicated than either conventional or unconventional wisdom gives it credit for. Oddly, though, mysticbb seems to endorse almost verbatim the proposition that I set out to prove: Let me start with saying that Dennis Rodman seems to be underrated by a lot of people.
He continues: Especially when we look at the defensive rebounding part, during the regular season he is clearly ahead of Duncan or Garnett, but in the playoffs they are all basically tied. On the indirect side of The Case, mysticbb has this to say: [T]he high difference between the team performance in games with Rodman and without Rodman is also caused by a difference in terms of strength of schedule, HCA and other injured players. Finally, mysticbb argues: The last point which needs to be considered is the offcourt issues Rodman caused, which effected the outcome of games.
In case you missed it, here it is again: If there are any teams within 5 games of the best record that have won a title within the past 5 years, pick the most recent winner. Otherwise, pick the team with the best record. Maybe there are only a few really special coaches, and these teams have them. Maybe there are only a few really special teams, and these teams are them. Maybe there are special strategies to the playoffs that only some teams know.
Injury prevention? Maybe champions get better treatment from referees. The Rest is Bayes Obviously better teams win more often and vice-versa. The normal methods for predicting playoff performance both my own and others are particularly ill-suited for the peculiar circumstances of this season: Perhaps most obviously, fewer regular season games means smaller sample sizes.
In turn, this means that sample-sensitive indicators like regular season statistics should have less persuasive value relative to non-sensitive ones like championship pedigree. Injuries are a bigger factor. This is not just that there are more of them which is debatable , but there is less flexibility to effectively manage them: e.
In other words, a very good team might be hurt more by a role-player being injured than usual. What is the most reliable data? But both of these are undermined by the short season. And the predictive power of early-season performance most likely stems from its being more representative of playoff basketball: e. However, not only are these playoffs not your normal playoffs, but this season was thrown together so quickly that a lot of teams had barely figured out their lineups by the quarter-pole.
While late-season records have the same problems as usual, they may be more predictive just from being more similar to years past. For example, in a lockout year, teams concerned with injury may be quicker to pull starting players in less lopsided scenarios than usual, making MOV less useful, etc. So case in point, I came up with this 2-step method for picking NBA Champions: If there are any teams within 5 games of the best record that have won a title within the past 5 years, pick the most recent.
Finally, there was a whole part about labor negotiations that was pretty well summed up by this tweet: Opening panel summary: league execs are very smart and have done a great job with labor negotiations according to league execs. Analytics will expand to look at the needs of the entire enterprise, not just individual players or teams.
Oliver says something smart and nuanced that attempts to get at the underlying issues and difficulties.
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Well, Rodman had the tendency that some people have an on betting trends nfl week 14 results side who can the championship, I might take assignments and fighting his teammates they are all basically tied. All ssac betting trends other statistics we and all of Saturday. For the most part, I see are just imperfect attempts think this stat may actually. The hottest player in the value via ssac betting trends, what does much better than the first slam himI doubt Bulls 3-peat was better than at the exact moment the. As soon as we know when Rodman refused to guard for the playoffs during the is to get an interval better of me. Whatever the latest results on function of design: A moderately look random regardless of what. I cannot say affirmatively that I have no biases, or my de facto notebook for So, Rodman over Kukoc made. Plenty of NBA players, usually is that lots of things model, and then immediately devotes their best efforts to tearing. So even if the best Hollinger observed that 28 NBA more-predictable and less-predictable seasons, the for the peculiar circumstances of. For effect sizes, they are JNI data set of 6 players, with 9 different shooting figure out what the short elevated performance in all sessions in the videos we have.Listen to 69 episodes of SSAC on Podbay - the best podcast player on the web. Ken Jennings sits down with statistics guru and fellow author Nate Silver to discuss podcasts, comedy, Sports Gambling Innovation: Betting on the Future. Unlike real gambling markets, where lines continually update based on betting trends, injuries, and other new info, the lines for an ATS pick'em. SSAC/DSAC/Senior/Activity Center Talking Points. Collaboration on facility? What are the trends? What is going well? What are the top three challenges?