Posts tagged: statistics

Bill Simmons is Carmelo Anthony’s Grandmother

By , July 15, 2014 10:38 pm

And so are a lot of New York Knicks fans.

Bill Simmons put out an article yesterday subtitled “With Carmelo Anthony staying with the Knicks, will we ever find out how truly great he can be?”  This type of ‘expert analysis’ becomes the basis of so many misunderstandings about the game of basketball and further illustrates how the media and fans are obsessed with perception than actual performances on the court.   The subtitle itself implies that Carmelo IS great, and subtly shifts winning responsibility off of Carmelo, and onto a variety of reasons from the common; his teammates, his coach, and the system he plays in, to the bizarre; his position (?), his contract extension (huh?), his draft selection (what?).  What is up with this invention of so many excuses when the numbers are staring you directly in the face?

Simmons ask the question, “How did Carmelo Anthony, only 30 years old and still in his prime, become the NBA’s most under-appreciated and misunderstood player?” To put it bluntly, Carmelo Anthony has not demonstrated the ability to be an NBA superstar, and is only misunderstood by this ill conceived perception.  He’s been a fringe All-Star at best, but somehow his stature has been elevated to that of a franchise player.  His career lines show he converts shots like an average shooting guard (45.5% FG%, 81.1% FT%), passes like an average big man (1.1 assist/turnover ratio), rebounds like a average small forward (6.5/gm), and plays defense like my uncle … and my uncle plays cricket. He doesn’t really excel in anything, except taking a lot of shots, and his career line looks pretty darn boring, nothing like an elite NBA player.

Simmons and Melo’s large immediate foster family will point to his career 25.3 ppg, and throw meaningless phrases around like “he can score from anywhere” and “he’s unstoppable.” Except they largely don’t realize that missed shots means missed scoring opportunities.  That means that there are GOOD points per game and BAD points per game, and since Carmelo’s shooting percentages aren’t elite, the volume of his misses diminishes the benefits of his makes.  How can someone be ‘unstoppable,’ which be definition implies a 100% FG%, when he can’t convert better than Chris Copeland? Even with the understanding that statements like that are figurative hyperbole, it’s not anywhere close to being a reflection of his play.

The next question Simmons poses is “Can you win the NBA championship if Carmelo Anthony is your best player?”  He answers himself with “The short answer: Yes,” but then goes on to compare Carmelo to a far superior player, Dirk Notwitzki.  This is like saying, “Hey, just give that yellow mustard a few more years to develop, surround it with organic ketchup and relish, and it’ll be just like Grey Poupon.” Introducing this comparison diminishes how great Dirk actually was before he won the championship, and somehow equating Dirk’s actual pre-championship play with Melo’s. Dirk’s Win Share/48, playoffs and regular season combined, hovered around .200 before he won in 2011, while Melo’s is about .130. In other words, Dirk is Polaner All Fruit; Carmelo is jelly.

To be fair, Simmons does go into great detail on why Dirk is better, using a plethora of actual game performances, none of which Melo has ever come close to replicating. However, he oddly tries to drum up a [weak] comparison of their best year playoff numbers, which he thinks are ‘not THAT far off.’  Have a look for yourself, and if you understand that being efficient is a serious advantage, it’ll be obvious why even these two lines are not comparable:

2011 Dirk (21 games): 27.7 ppg, 8.1 rpg, 2.5 apg, 49-46-94%, 8.9 FTA, 25.2 PER
2009 Melo (16 games): 27.2 ppg, 5.8 rpg, 4.1 apg, 45-36-83%, 9.0 FTA, 24.3 PER

Now, this was by far Carmelo’s best play-off run, and his Win Share/48 showed that as he checked in at an All-Star Level .201.  In his prior five play-off appearances, he was shockingly bad, and in three (3!) of those years, he actually had a negative Win Share/48.  That pretty much means that Denver would have been better off if he didn’t play at all!  And if we really look at 2009 playoff Carmelo, we’ll see that while Simmons tries to deflect the blame to his ‘substandard’ team and J. R. Smith, let’s not forget that this is what Melo’s stat line was against the Lakers.  Not only are his rebounds and assists down, his shooting from the field was abysmal against the stiffer competition.

2009 Melo vs Lakers (6 games): 27.5 ppg, 4.8 rpg, 3.7 apg, 41-25-83%, 20.0 FGA, 12.5 FTA

Simmons likes to point to team composition as an issue, but even in Melo’s best playoff performance, he wasn’t the best player on the team.  That honor went to Chauncey Billups, who put up an elite Win Share/48 of .249 in the playoffs after a season Win Share/48 of .176 (note Denver’s season Win Share/48 leaders – Nene .177, Billups .176, Anderson .176, J. R. Smith .124, and finally Carmelo hovering around league average .105).  We tend to think of those Denver teams as Carmelo’s teams, mainly because of his inflated point per game numbers, but the reality is that those were Billups’ teams, by both contribution to winning and by his NBA Finals MVP pedigree.

The fans and the media overvalue points per game, and that’s likely why the Melo elevation is happening.  Instead of using this oversimplification stat as gospel, they should think about how those points are scored, and realize that a missed shot is more detrimental than a made shot is rewarding.  This is because there is a cap on how many shots a team can take in a game, so any missed shot is a missed opportunity to maximize your team’s points and win.  When your best player is not an incredibly efficient volume scorer, your team doesn’t have much of a chance unless that player is also incredible possession guy (elite rebounder/defender) or team FG% enhancer (elite assister).

Carmelo has been neither of those in his entire career, so it’s a wonder why fans and writers constantly elevate him to some greatness he’s never showed a glimpse of consistently achieving.  In fact, at least in terms of win contribution, Carmelo has been the best on his team only once, and that was with last year’s Knicks.  Why don’t we compare him to a player that was actually very similar to him, someone like let’s say … Glenn Robinson.

Big Dog (career): 20.7 ppg, 6.1 rpg, 2.7 apg, 46-34-82%, 17.6 FGA, 4.4 FTA
Melo (career): 25.3 ppg, 6.5 rpg, 3.1 apg, 45-35-81%, 19.7 FGA, 7.7 FTA

The ‘Big Dog’ played the exact same style of basketball in the 90s as Carmelo plays now.  He was considered an incredible scorer, a volume shooter not known for much defense, with a career mediocre win/loss record.  His numbers are eerily similar to Melo’s, with the exception of points per game, which upon normalization, is nearly identical. Had Robinson been a shot consumer in the same neighborhood as Melo, assuming Robinson’s efficiency was roughly the same, he would suddenly be a 25 ppg scorer. Watch some of his highlights for yourself; the guy could get a shot off from anywhere, and back then, people thought he shot too much.

Robinson only made the playoffs three times as a big minutes contributor, and those appearances include two 1st round knock-outs, and one deep run to the Eastern Conference Finals.  Does that sound like anyone we know?  Robinson did win an NBA Championship with the Spurs as a deep bench guy, which better answers Simmon’s question, “Can you win the NBA championship if Carmelo Anthony is your best player?”

The short answer: No.

Carmelo has made some strides in improving his game, and while the Knicks supporting cast is not fantastic and their front office has been hauntingly bad, we have to start holding him accountable for his on-court performance. This pervasive thought that because you’ve looked dominant in small samples, means that you actually are dominant on average, is foolish and absurd.  Until Carmelo proves he can do SOMETHING better than average, whether it’s increasing his assists, or controlling the glass, or play great team defense, or just simply shooting the ball better, any team that choses to employ him is destined for disappointment.

What Are You Talking About Bill Simmons Bonus Material:

“Carmelo? He’s 92 percent as frightening as 1984 Playoff Bernard was.” – King averaged 7.5 more points per game, shot a whopping 12% better than Melo, and was about 16.4% better as per Win Share/48. How does one quantify ‘frightening’ anyway?

“He’s just playing in a more difficult league — better scouting, better game planning, better defenses, better athletes, better everything.” – Era comparisons like this are hugely misleading because Simmons is singularly applying to Melo the advancements that apply to the entire player pool. Melo has benefitted from better scouting and better game planning as much as he’s been hurt by it. Better defenses are largely rules-based (i.e. hand-check rule, zone defenses) which again, would apply to everyone. Better athletes are probably not true based on this TED Talk which shows the bulk of ‘improvement’ is really due to technology and specialization.

“That pathetic Knicks team didn’t employ a single creator who could get Melo wide-open jumpers off slash-and-kick drives.” – If the point guard is to blame for this last year, that either means Kidd, Billups, and Lawson were guilty of the same thing since Melo’s shooting has been roughly the same, or Carmelo is just not that great of a shooter Simmons thinks he is.

“Just a slew of possessions, one after the other, with everyone standing around waiting for Carmelo to do something. They were like the pickup team from hell, only Carmelo couldn’t just throw the game and hop on someone else’s team.” – Anyone that has ever played pickup basketball has at one point played with a guy who ball hogs on offense, and doesn’t get back on defense. Everyone else ends up standing around, because you know that you will not see the ball no matter how good your back-door cut is or how wide open you are in the corner. This sounds more like what happened to the Knicks, which Tyson Chandler said in the playoffs. I’m not sure Simmons has ever played pickup basketball after an analogy like that. Plus, didn’t Melo basically try to hop on someone else’s team by opting out? No takers? I’m not surprised.

Josh Freeman or Aaron Rodgers, A Friday Night Lights Story

By , December 18, 2012 1:02 pm

Week 15.  Fantasy Football Playoffs.  Do I start Josh Freeman over Aaron Rodgers?  I already know the immediate answer.  No.  Absolutely, completely, without a doubt, No.  You are an idiot for even thinking otherwise.  It’s Aaron Rodgers!  He’s the best QB in the game!  You go with the horse that got you there.  Rodgers is a stud and what has Josh Freeman ever done?  I heard it all, a resounding ‘Yes’ to Aaron Rodgers …  and I started Josh Freeman instead.

Everyone I had talked to hadn’t looked at the numbers for these two quarterbacks this year, and practically no one considered the defenses both these guys went up against.  They heard the name Aaron Rodgers, and not only assumed he had been vastly superior to Josh Freeman this year, but also assumed that fantasy scoring systems reflect the true skill level of a player.  The reality is that Aaron Rodgers is probably the best skilled quarterback in the NFL, but when placed within the scoring system of fantasy football, Josh Freeman certainly becomes comparable.  Side Note:  When considered in terms of winning NFL games, Eli Manning tops all of them and is the perfect example of how real world winning doesn’t necessarily translate to fantasy sports winning.

For the season, Rodgers ranked 5th in scoring while Freeman checked in at 12th overall, with the  difference between them being 2.62  fantasy points per game (standard ESPN scoring system).  In a 12 team league, both quarterbacks would be considered QB1s based on the points they put up, so there is a conversation to be had here.  Josh Freeman also had a lower standard deviation in his scoring, which means that he was more consistent in achieving 16.23 points per game than Rodgers was in achieving 18.85.  In other words, Freeman statistical performances were less risky.

When you begin to look at the more recent history, the QB comparisons get a lot closer than you’d expect.  From Week 5 to Week 14 (9 games), Freeman averaged 18.87 fantasy points per game (fppg) with a 5.56 standard deviation, while Rodgers averaged 19.67 fppg with a 9.00 standard deviation (mind you I’m not cherry picking time frames here; Rodgers scoring numbers went up and it included both players season high point outputs).  When looking at this time frame, we are now talking about one quarterback who registers less than a point more per game versus one that is a whole lot less risky.  Uh oh, looks like we have a quarterback controversy brewing.

The last thing I looked at were the match-ups, and Freeman won big time in that respect.  New Orleans had given up the most fantasy points to opposing QBs in the league, to the tune of 20 fppg, which represented a +1.94 deviation from the league average of 14.84 fppg.  They had given up better than average performances in 11 out of 13 weeks to quarterbacks including the likes of Phillip Rivers, Michael Vick, Carson Palmer, and Colin Kaepernick.  The Bears, Rodgers’ Week 15 opponent, was the exact opposite, giving up a meer 9.8 fppg (-1.89 standard deviation from league average), best in the league which included a 10 point game to Rodgers back in Week 2.  It is also worth nothing that Freeman had his season high 29 against his Week 15 opponent, the New Orleans Saints.

I used these statistics to build a very simple model to give me a sense of projected fantasy points for Week 15 (actually I considered a much more complicated statistical model normalizing all of these stats based on who actually played who, but I’ll leave that out because I don’t have a lot of faith that this math illiterate county can even follow this simple model based on averages and deviations).  I included the four other quarterback controversies I was considering for Week 15, which was the reality of all four leagues I was in this year:

Each color band represents the choices I had to make, and in each case, the higher seasonal ranking QB was listed first.  All four scenarios represent the exact same situation as Rodgers/Freeman, where we have a well established, well-known stud versus a relatively unknown and unproven commodity.  Using the simple model, in all cases the projected points suggested that you should bench your stud in favor of his back-up (the absolute points don’t matter in the comparison, and in the more sophisticated model, Rodgers was more in the 12 – 15 fppg range with Freeman in the 21 – 24 fppg range).  Reality seemed to have beared that out as well, since in 75% of the scenarios, QB2 outscored QB1.

So yes, I stand by my decision to start Freeman over Rodgers and with this type of analysis, I will get it right more often than not (hi Bill Belichick).  In the particular league where I had the Freeman/Rodgers choice, I lost by less than 13 points, so mostly everyone will point to that example as the reason to always start your studs.  However, the reality is that setting your line-up to maximize your points and what actually happens in the game are two separate and unconnected things.  The Packers play-calling in game situations doesn’t dictate they should throw to the endzone because Aaron Rodgers is my starting fantasy QB.  Above and beyond that, I might not have even won with Aaron Rodgers if I happened to run up against Russell Wilson (especially a downward single deviation that Rodgers has put up in past fantasy playoffs, in particular Week 14 against KC in 2011, Week 14 and 17 in 2010, and Week 16 and 17 in 2009).

In fact, it’s not even the Freeman/Rodgers decision that lost that game for me, it’s how far from the norm Freeman AND the New Orleans Defense deviated from their histories!  If the New Orleans played to the league average of 14.5 points given up, which was one standard deviation on the PLUS side for them, I would have won.  Had Josh Freeman performed within one DOWNWARD standard deviation of his stats over his last nine games, he would have had 13 points and I would have won.  If we could run the  Josh Freeman versus New Orleans Saints scenario a million times, 84% of the time Freeman would have put up enough fantasy points for me to win.  What we saw on Sunday was the perfect storm, a statistical aberration which resulted in a two and half standard deviation from the norm, a .5% scenario that no one is immune to.  Frankly, you don’t set your fantasy team, or make most decisions in life, based on .5% outcomes.  You buy insurance for them (or against them).

For the most part, these simple statistical predictions show themselves in the real world, given you have enough scenarios to examine.  In my limited fantasy world, I had four leagues where this case existed, and in three of them it proved to be correct.  In a broader history, a 36 – 17 regular season record, three first round byes, and two trips to the finals is probably better than most people who play fantasy football can represent.  If I had brazenly played Rodgers this week and snuck out the win, it would be statistically obvious to play him against the 20th worst pass defense in the league in Week 16.  The point is that sometimes you have to go Saracen when the analysis says so and put your ultra skilled J.D. McCoy on the bench even if it is in the playoffs, (*SPOILER ALERT*) … and sometimes you still just fall short.

What was Linsanity?

By , May 17, 2012 3:16 am

Now that the Carmelo led Knicks have been dumped in the first round of the playoffs yet again, I thought I’d reflect a bit on this big time up and down season we’ve gone through (if you didn’t catch the roller coaster, here’s the New York Post’s Knicks back page covers for then entire season).  The Knicks finished 36 and 30, securing the 7th seed in the East and then promptly got dismantled in 5 games against the heavily favored Heat.  While the play-off result was expected, the next few blog posts will look at the play of the Knicks ‘stars’ over the season to try to distinguish what led to their streaks of success and abysses of failure. If you couldn’t tell from the title, this post will focus on Jeremy Lin, or according to ESPN’s writing department, the only Asian who can drive.

I fortunately had the opportunity to watch four of Jeremy Lin’s games live at Madison Square Garden this year.  Those games were his breakout game against the Nets, his huge game against the Lakers, the blow-out win against Sacramento, and the sensational Knicks bench led comeback against Cleveland.  When watching him play, I was impressed with his ability to finish near the basket while drawing a lot of contact, and how great he plays the pick-n-roll.  Yes, there were times he looked overmatched, like against Miami, Boston and the 2nd Nets game, but let’s be fair, there are a lot of 2nd year players that are overmatched by the likes of Wade, Lebron, Pierce, Rondo, Garnett and Deron Williams. Now, what really stood out to me was his decision making, and although the high TOs suggest something different, he seemed to never really force a really bad shot and had consistently found guys on the court where THEY are their ultimate best.  We saw the best of this when he played with a scrub starting line-up, and this characteristic above all is why I feel he has a chance (not a guarantee) to be something special for many years to come.

Now I come from a pretty analytical background, and the first thing you learn when approaching measurements of anything is that eye witness testimony is the WORST type of evidence you can possibly present.  It’s riddled with personal biases, and mood swings, and unfounded perceptions of demographics and one’s ability to access something. ‘Moneyball‘ proved that.  Eye witness testimony in basketball are things like ‘explosiveness’ and ‘creativity’, which often account for impressive highlights (read as one-off events), but rarely correlate at all to long-term performance or success.  We are going to try to do this by looking at the numbers, the data, the statistics that matter.  And the question we’ll try to answer is “Is Jeremy Lin a Top 10 PG?”

I’m going to concede six PGs in this analysis because they have had a pretty good history of performing at a top 10 level and they have a chance to do the same over the next six years: Chris Paul, Deron Williams, Tony Parker, Rajon Rondo, Russell Westbrook and Derrick Rose.  That leaves about ten guys left to occupy the final four slots, whose 2012 numbers have been normalized to 35 minutes a game to show how they compare to each other, ignoring obvious things I don’t want to do the math to control for like the teams they play for or the types of offensive systems they run.  I intentionally left out FT% because it is inherently reflected in Points, 3pt% because it is inherently reflected in FG%, and Turnovers because that would make it very easy to pick out Jeremy Lin.

I highlighted the top three performers in each category in green and the worst in red to make it easy to reference.  The first thing that jumps out is that none of these guys are THAT statistically different from each other, which makes it very difficult to make a case one is better than the other.  Player B scores the most and shoots it pretty well, but falls in the lower tier in assists and steals.  Player J racks up the assists, but doesn’t shoot that well, score, or block shots.  Player I isn’t in the top three in any category, but generally in the middle of pack of all the categories.  Player E tops in shooting and 3s, and doesn’t hurt you anywhere.  If you can make a clear cut case anyone here is better than anyone else, please do so in the comments.

Can you pick out Jeremy Lin?  What if I told you the other players were John Wall, Brandon Jennings, Ricky Rubio, Mike Conley, Kyle Lowry, Ty Lawson, Kyrie Irving, Goran Dragic, and Stephen Curry?  It’s not that easy is it.  Seriously, get a piece of paper out, rank the stat lines, and assign a player to each one.  I’ll wait. I need to get a beer anyway.

Okay, you good?  Here is the list again identifying each player and adding Turnovers.

A bit surprising?  How many of you had Player F as someone you thought was better than Lin?  How many of you had Player F in your top 5?

To narrow this down further, if we remove the guys with more negatives than positives (Conley, Dragic, and Rubio), and put Steph Curry into the top 10 for not having any negatives (although he’s more a SG than a PG), then we are left with Jennings, Irving, Lawson, Wall, Lin, and Lowry for the last three spots.  Note only two guys left have more positives than negatives, and Lin wins there at 4 – 2 to Irvings 4 – 3.   So after some basic statistics, we have Lin in some contention for a top 10 PG, with the most glaring negative aspect of Lin’s game being the high turnover rate.

To look at this further, I thought I’d break down Lin’s 2012 season into three basic eras:  The 12th Man, Linsanity, and Melodrama.  The 12th Man era was basically the games he played in blow-outs, Linsanity was when he played all his games without Carmelo and only two with Amar’e, and Melodrama was the games played when both Melo and Amar’e were back. Here’s how those games broke down:

The Linsanity Era was tremendous.  I mean, averaging 25 points, 9 assists, 4 rebounds, 2.2 steals, and 1 three while shooting 51% from the field is in the stratosphere of Chris Paul, Deron Williams, and Nash MVP years.  None of the guys left on our list have ever had a stretch like that.  The turnovers were atrocious, but remember this was a stretch where the Knicks starting line-up was Lin, Landry Fields, Bill Walker, Jarred Jefferies, and Tyson Chandler.  This was a stretch where the bench was mainly Steve Novak, Iman Shumpert, and Mike Bibby (yes, this was before J.R. Smith and Baron Davis).  Where are the ball handers in this line-up?  It’s no wonder that his turnover rate was so high given how little possession guys were on the court.  And the most important, ridiculous, impressive, lunatical (made-up word) part of this stretch was WITH this god awful team, the Knicks were 8 and 1!  8 and 1!  Let that sink in.  8 and 1, beating play-off teams like the Hawks, Lakers, Jazz, and Mavericks.  8 and 1.

The next 15 games started with a dysfunctional, lack of effort, D’Antoni/Melo feuding shit show where the Knicks lost 6 straight which led to D’Antoni’s resignation.  Assistant Coach Mike Woodson took over and the Knicks caught fire, winning 6 of the next 7 before Lin’s season ended due to knee surgery.  During this time, Lin seemed to have done a pretty good job adjusting to an isolation system featuring Melo, and put up 15 points, 7 assists, 4 rebounds, 2 steals in about eight less minutes of playing time a game.  More importantly, with the ball in Melo’s hands more, we saw Lin’s TOs dropped by two (although some of this could be attributed to less minutes on the floor).

So is Lin a Top 10 PG?  I’m not ready to put him there absolutely yet, in fact, of the guys left, I would probably put Ty Lawson in the Top 10 before anyone else.   The point here is that while I can’t say for certain that Lin is a top 10 PG, he has made an equal case, if not stronger case, than the rest of the guys on the list.  It’s not a clear cut thing that Lin can’t be a great PG.  He’s put up elite stretches with limited talent and WON the bulk of those games, something guys like Lowry, Conley, and Jennings haven’t come close to in many more opportunities.  Guys like Wall, Dragic, Rubio, and Irving, like Lin, have had limited opportunities to prove if they can be elite, but there’s no reason to automatically call them better because of ‘creativity’ or pedigree.  Honestly, I don’t know what gives NBA players that winning edge, but for now, let’s just say Jeremy Lin’s superior Lintellect has made me a believer.

But speaking of pedigree:

Lin was First Team All State and Northern California Division II player of the year his senior year in high school while winning the national championship.

Lin’s Junior year at Harvard, he was the only NCAA Division I player to rank top 10 in his conference in points, rebounds, assists, steals, blocked shots, FG%, FT%, and 3Pt% while making All-Ivy League First Team.  That year he had 27 pts, 8 assists, and 6 rebounds in a win over 17th ranked Boston College (who had knocked of No 1. North Carolina just three days earlier).

Lin’s Senior year at Harvard, he averaged 16.4 points, 4.4 rebounds, 4.5 assists, 2.4 steals and 1.1 blocks, and was again a unanimous selection for All-Ivy League First Team.  He had 30 points and 9 rebounds against the 12th ranked UConn Huskies.

In the NBA D-League Showcase, Lin averaged 21.5 points, 6 rebounds, 5.5 assists, and 3.5 steals.  In 20 games in the D-League, Lin averaged 18, 5.8 rebounds, and 4.4 assists.

During the NBA lock-out, Lin played in the ABA Club Championship in China where he won the MVP of the tournament.

During his season with the Knicks, Lin was sent to the D-League where he promptly put up a monster triple double of 28 points, 11 rebounds, and 12 assists.

A few days later, Linsanity started and really only one man saw it coming years before.  Read his 2010 Draft Preview of Jeremy Lin and check out this great Linfographic for some of the records Lin has set in the NBA.

“So this (Fantasy Baseball) team is perfect”

By , March 2, 2011 12:50 pm

It’s almost fantasy baseball time again and although my slow starting roster made a respectable run to finish in 4th, I didn’t re-capture the title which I won in 2008.  The consolation prize is that our commissioner, Hans Ruddilicious, for the 9th straight season did not win a title, settling for his record breaking/setting fourth 2nd play finish (see Bills, Buffalo – 1990, 1991, 1992, 1993).

Last year’s champ christened this year’s bulletin board with this fine piece of smack talk:

“i just took a quick glance at the initial rankings and the top 10 players looked like the 2010 Championship Bag of Poo lineup. weird…”

This got me thinking about what it would take to build a team that would perfectly beat any other roster of players, even if you were able to pick the same players (barring obvious ties).  It would seem that a baseball season, with 162 games would be enough to do statistical analysis, although we’d probably want to dampen the volatility a bit by looking at it on a weekly basis.  In this case, building a Monte Carlo simulation model and running thousands of scenarios could give you a good idea of which players would be best to wager a high draft pick on.

This would be a pretty good start, but what about other factors like injury proneness, skills progression/deterioration, team/lineup changes, and to a lesser degree strength of schedule and home field advantages/disadvantages?  It would be a pretty cool analysis to do to try to capture a bit of an edge in your fantasy league (i.e., look for Give It More Hand to return to the top of the standing this year).  Plus, it would be a pretty fun to put to the test our fantasy expertise, especially for bragging rights.

I came across this site that is bringing this concept to life, although not for fantasy baseball.  fantisserie.com looks like a game that lets you put your fantasy shit-talking to the test in a weekly competition for cash prizes.  The entry free is pretty nominal, about the cost of most iPhone and Android apps or a drunk impulse purchase of a Slim Jim from the local deli at 4:30AM, and they are offering a pretty large cash prize to anyone that can hit perfection for a week.

fantisserie

Not a bad trade-off, much like indigestion for the tasty deliciousness of previously mentioned 4:30AM Slim Jim.  Gotta have beef, gotta have spice, need a little excitement.  SNAP INTO A SLIM JIM.

How Bad is my Fantasy Baseball Team?

By , May 3, 2010 8:54 pm

I’m sitting in 9th place, 67 whopping points behind the league leader after the first month of the baseball season.  That’s pretty awful.  The question is, do I have any hope?  I decided to take a statistical approach to see if I should jump ship on my team, or try to hold through this miserable start.

The first thing I looked at was the historical monthly averages and standard deviations of the players on my team.  When I did this, I got a pretty ugly picture for April, but some hope for the future.

The green represents the categories where my players are performing better than their historical averages, while the red …. well, you know.  A lot of red on this chart … a lot.  Should I be selling low right now?

The data is telling us that April 2010 was a uncharacteristically bad for my team when compared to typical months in their career.  If statistics hold, I should expect a reasonable bounce back for nearly all my players for the rest of the year.  I should hold out for a little bit longer before making any significant panic moves, but I will certainly be looking for improvements across the board.  I’ll be keeping a close eye on riskier players like Ichiro (age) and Hamilton (injuries), and they will be the guys I’d look to move for a younger/healthier equal poor starting ‘star.’

What have I done so far?  I’ve moved Molina for the ‘no timeshare’ Miguel Olivio in Colorado, and I’ve gotten Lance Berkman back to take the spot of Curtis Granderson (who actually wasn’t that much of a drag on my team since I only play him against right-handers, check the splits for his career).   If Rich Harden can put it together, Grienke get’s some offensive help, and Dave Duncan has really fixed Brad Penny, I might have a fighting shot.

Your turn.  Who would you target right now in a trade, and who would you move off my team?

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Stats in Sociology – Not Boring Version

By , December 30, 2009 11:23 am

Hans Rosling shows you how to to make stats cool again.  Visualization of data is a great learning tool, and creating ways to do this will be of great value to those with less mathematical sophistication than others.

The insights that Hans Rosling was able to illustrate in this presentation were pretty amazing as well, and definitely surprising to see how much the world has leveled in the past 40 years.  Check it out:

Bill Belichick + Statistics = Usually Good Outcomes

By , November 16, 2009 3:48 pm

I’ve been a die hard Giants fan for as long as I can remember, and although I was technically alive and barely remember parts of the 86 season (and nothing from that Super Bowl), the 90 Super Bowl team made me love the G-Men .  They had me at Mark Ingram’s 3rd and 13 conversion.

Bill Belichick was the Giants’ defensive coordinator in that Super Bowl and designed a genius defensive game plan, predicated on the statistics of his defensive unit.  Knowing that Jim Kelly and the Buffalo Bills could rip apart the Giants’ secondary, he had his defensive linemen and linebackers give up yards on 1st and 2nd down.  He believed that this would dictate that Buffalo would run the ball, rather than pass in longer 3rd down situations, a place where the Giants were statistically strong in all season.

His gutsy calls are not relegated to just defense.  The 07 Patriots’ offense was a great example of not playing into defenses strengths (case in point, going to five WR sets against the top ranked Minnesota run defense), and taking gratuitous unsportsmanlike advantages of your strengths when the game was well out of hand.  Gratefully honor prevailed and the Giants laid the smack-down on the Patriots to win their 3rd Super Bowl and squash a rather presumptuous book before it made its way onto bookshelves.

Last night’s Patriots Colts game is just another way Bill Belichick makes football analysts (like Mike Francesca) and arm-chair quarterbacks look like idiots.  It was absolutely the right move to go for it on 4th and 2 and the odds were completely in his favor, as described on “Advanced NFL Stats:

“With 2:00 left and the Colts with only one timeout, a successful conversion wins the game for all practical purposes. A 4th and 2 conversion would be successful 60% of the time. Historically, in a situation with 2:00 left and needing a TD to either win or tie, teams get the TD 53% of the time from that field position. The total WP for the 4th down conversion attempt would therefore be:

(0.60 * 1) + (0.40 * (1-0.53)) = 0.79 WP

A punt from the 28 typically nets 38 yards, starting the Colts at their own 34. Teams historically get the TD 30% of the time in that situation. So the punt gives the Pats about a 0.70 WP.

Statistically, the better decision would be to go for it, and by a good amount.”

It didn’t work out for Bill last night, but the decision was sound, and in the long run, he’s going to come out on top more often than not.  It’s why he’s a great NFL coach, and we shouldn’t be convinced that our “conventional” wisdom is better than his statistical prowess.  Just be content knowing that he’s a prick and move on.

Predicting the World Series using Python

By , October 29, 2009 7:45 am

Last week, I’ve started to learn Python through a peer-to-peer learning session set up through nextNY.  The material that we’ve gone through has made learning programming very easy to wrap our heads around, and the environment of cooperative learning has been awesome.  I’m looking forward to being a Python ninja* pretty soon.

With four and half chapters of Python at my disposal, I wanted to put my skills to the test.  Since I’m a huge baseball fan, I thought I’d try my hand in simulating who would lose the World Series this year, a pillow-fight match-up between the New York Yankees and the Philadelphia Phillies.

The first thing to do was to crunch the numbers.  Crunching the numbers means exactly that, figuring out the probabilities of events occurring over a seven game series.  I incorporated things like Ryan Howard’s immense strike-out rate, Derek Jeter’s incredible lack of range at shortstop, and Brad Lidge’s ninth inning ERA.  I also made sure to incorporate correlations, or how related each variable is to each other.  Funny enough, the highest correlation I found was between having a runner on first base with less that two outs in the seventh inning onwards and Arod weakly grounding into a double-play.  Numbers never lie.

Now this got me a pretty good picture of who would lose the World Series, but I hadn’t taken into consideration the qualitative variables, the intangibles, the “Cole Hamels’ is a play-off pitcher” and the “Mariano is unhittable in the World Series” bullshit bullshit.  These are usually the ‘statistics’ that overzealous fans throw out (with no meaningful data except their distorted memories) as their defense to a player’s immortality.

The classic intangible lies on the shoulders’ of the Yankee captain, Derek Jeter, a ball player that seems to find himself at the right place at the right time in the postseason.  Yankee fans have constantly spouted his ‘greatness’, and refuse to admit that he was horribly out of position on the Jeremy Giambi play at the plate, and doesn’t even register as having the highest batting average in a World Series (that designation goes to Billy Hatcher who hit a sickening .750 for the Reds in 1990 in 12 ABs).  Heck, Jeter doesn’t even deserve the nickname “Mr. November” for his play in the 2001 World Series.  He had 1 HR, 1 RBI, and 2 runs scored in November, numbers that were almost matched by a pitcher for the Arizona Diamondbacks (1 RBI and 2 runs scored).  Oh, and that pitcher also won two potentially series ending games in two days that November with a 2.22 ERA, .96 WHIP, 8Ks in 8.1 innings.  Derek Jeter, I’d like you to meet the real “Mr. November,” Randy Johnson.

Okay, so I wrote my little Python program to capture all of this.  The stats, the pseudo-stats, the Phillie Phanatic’s rants, and the countless times we’ll hear “26 World Series rings.”  With so many probabilities and interactions, this program chugged along for two days, and finally, yesterday before the first pitch, I got the result:  Value Error: Let’s Go Mets.

*Looking forward to the day when ninja is not used in start-up world employment searches and reverts back to its original awesomeness of stealthy nighttime assassin.

Blackjack, Basic Strategy, Battle of Wits – Part III

By , August 5, 2009 12:28 pm

Have you ever been on a blackjack table and accidentally hit a hard 14 with the dealer showing a 5 while playing basic strategy?  Replay it in your mind, you bust on the King, dealer makes his 21 on a 6, the entire table gives you the death stare, curses your first born, all while mumbling under their breath, “Never hit on a hard 14 with the dealer showing a 5 idiot. That King was the dealer’s bust card. We all would have won.”  Tough room.

First things first, those people have no idea what they are talking about.  There is no such thing as “that was the dealer’s bust card.”  The deck doesn’t know whether the dealer or the player is hitting or staying and the cards don’t change because of how someone plays their hand.  The probabilities that guide basic strategy haven’t been altered because someone does not make the optimal play and theoretically the dealer still has the same likelihood of busting (in practice though, since the deck has a fixed amount of cards, the distribution of remaining cards changes the underlying probabilities of basic strategy.  Card counting attempts to exploit this by identifying random deck distributions that happen to have a large amount of 10-value cards remaining).

The important thing to remember here is that basic strategy gives you the probabilistically best play given that the deck has a RANDOM DISTRIBUTION OF CARDS.  That means that if the deck is not random, basic strategy might not be the optimal play.  So what everyone should consider before baptize themselves in the holy waters of basic strategy is what it takes to make a deck random (and who controls what it takes to make a deck random).

To make a single deck random, the deck must be riffle shuffled about 7 times.  Since suit doesn’t matter in blackjack, and K, Q, J, and 10 hold the same value, you actually need to shuffle a single deck less, about 4 times, to make it random.  Most casino blackjack tables play with 6 or 8 decks at once which are shuffled together and played from a dealer’s shoe.  In order to randomize a shoe of 8 decks, it takes about 12 riffle shuffles.  Does your casino shuffle a shoe 12 times?  Probably not.  Most casinos shuffle a shoe 4 times, and that has some interesting implications when an entire table is playing basic strategy.

So let’s take a look at what happens to a shoe when the entire table is playing basic strategy.  The first thing is that anyone that has a strong hand on their first two cards (17+) is instructed to stay, and their cards remain on the table until the deal is over.  Players with weak hands play out their hands, and if they bust, the cards are removed from the table and placed in the discard shoe.  This begins to create layers of cards in the shoe; clusters of low cards placed on the shoe first, followed by clusters of high cards that were left on the table.  Since most casinos do not shuffle the shoe enough times, these layers loosely exist in the new shoe, and are further propagated when the entire table plays basic strategy (some people attempt to exploit this by using a technique called cluster counting).

Clustering of cards creates decks that are not random, which is one of the critical assumptions that basic strategy is built on.  This creates opportunities for dealers to win/push more hands than basic strategy predicts.  During a high cluster deal, a dealer is likely to have high cards to push, or even beat, the tables “strong” 19s and 20s.  In the case where low card clusters are being dealt, a dealer will likely have a low up-card, a situation where basic strategy dictates to hit against until about 14.  The thing is that since it’s a low cluster deck, the dealer  has a better chance to make a hand!  The player also has a better chance to make a hand, but basic strategy actually advises them not to try.  INCONCIEVABLE!

Basic strategy is still by far the best way to reduce the house odds, but since decks are not completely random, there is certainly room for improvements in game play.  For example, in high cluster deck situations, it may be worthwhile to split face cards, while in low cluster situations, taking another card to try to make a better hand may be your best bet.  Playing this way may add a little bit more excitement to the rule-based approach of basic strategy as you’d be trying to exploit the rest of the table playing the basic strategy system.  And if it pisses anyone off at your table, just turn to them and say “You fell victim to one of the classic blunders!  The most famous is never get involved in a land war in Asia, but only slightly less well-known is this: never play basic strategy against a dealer when deck isn’t random!”  I’d get a kick out of that if I heard that on a blackjack table.

SmackDown Headliner – Google VS Facebook

By , June 23, 2009 12:26 pm
Me at 7, with bigger guns

Me at 7, with bigger guns

I haven’t watched WWF, or WWE, or Friday Night Smackdown since I was a kid (see right), but after reading Wired magazine’s article on Google vs. Facebook, I could not help but think about, in my opinion, the greatest wrestling match of all time.  This battle pitted the up and coming, wildly popular, eccentric and electric young superstar against the stalwart, power punching, mega-myth champion of the world.  Of course, I’m talking about the headliner at WrestleMania 6 where the Heavy Weight Champion of the World Hulk Hogan fought the Intercontinental Champ, The Ulllttiiimmmatteeeee Warrrrrioorrrrrrr!

Champion against champion, title for title, that’s what it’s all about.

Google and Facebook are waging their own war on shaping what the Internet’s future will look like.  They both have an underlying mission to share information, but their core approaches and visions of the web are very different.  Google has historically viewed the web as the great equalizer, the place where information can be accessed by anyone and everyone, and that information can be efficiently found by harnessing the power of cold, hard algorithms.  Facebook sees the web not as the source of information per say, but rather as the medium for which people can share information across their social net.  Instead of relying on complex math necessarily, Facebook puts the power of human sharing in the forefront of spreading information.

Both of these approaches have their place on the web.  What good is a platform to share information easily from the people that matter most if the people that matter the most can’t find the information in the first place, and vice verse?  In my mind, the bigger challenges lie in front of Facebook, because the future of sourcing information from hundreds of friends (if not thousands for the Facebook junkies “power users”) will come down to powerful ranking, grouping, sorting, and prioritizing algorithms, a space that Google has done very well in.

“So wha’cha gonna do brother … when the Hulkster (read as Google) comes for youuuu (read as Facebook)!”  Well, Facebook has been able to pull some ex-Googlers into their shop, to a tune of nearly 9% of their staff, and they have a virtual lock on the social network space (although I begin to worry about the hipness of it when my parent’s generation is “friending” me).  As difficult as it may seem, they may be putting together the pieces and the relationships to really challenge Google’s web dominance.  And maybe, just maybe, they’ll have enough to gorilla slam the powerhouse, avoid the leg-drop, and big splash their way to top, just like the greatest character wrestler of all time was able to do.  R.I.P. The Ultimate Warrior.

Bonus Footage:  Top Ultimate Warrior Promos Ever

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