Wednesday, September 17, 2014

Follow Perduco Sports' Fantasy Football Team

On opening weekend, we were able to attend the Fantasy Football World Championships sponsored by Scout.com and presented by FFToolbox.  During the 3 day drafting event, a slot came open in one of the RotoBowl drafts and I was asked to step in.  Using only my wits and my iPhone at battery level of 10%, I managed to scrape through the 20 round PPR draft.

Throughout the football season, I will demonstrate the use of our amazing set of fantasy tools and how they are utilized to help set my lineup.  For the first two weeks, I had to play catch-up and quickly make my own judgement on who to start.  Week 1 went well!  Week 2 - not so much, but that happens when A.J. Green leaves the game without touching the ball once!

I will be back with my Week 3 lineup and how it came to be.  In the mean time, here is the roster that I have to play with (starting lineup requires 1QB, 2RB, 3WR, 1TE, 2Flex, 1DST and 1K):

QBs:

  1. Drew Brees
  2. Eli Manning
RBs:

  1. Frank Gore
  2. Reggie Bush
  3. Chris Ivory
  4. Shonn Greene
  5. Dexter McCluster
  6. Stepfan Taylor
  7. Alfred Blue
WRs:

  1. A.J. Green
  2. Roddy White
  3. Anquan Boldin
  4. Andrew Hawkins
  5. Wes Welker
  6. Terrance Williams
  7. Danny Amendola
TEs:

  1. Jimmy Graham
  2. Coby Fleener
DST:

  1. Houston Texans
PKs:

  1. Steven Hauschka


Friday, July 18, 2014

Working in Sports Analytics

I just read this great posting by Ben Alamar (Cleveland Cavs) here!  I was very impressed with his write-up and agree with almost every line.  I really like his four points and want to piggy back on them with some of my own comments:

1. Eyes open
2. Technical skills
3. Audience
4. Do something

1. In terms of having your "eyes open" - this is probably the most shocking thing that newbies to the sports world hear.  Everyone thinks - "These teams have millions, if not billions, I am going to make good money!"  Wrong!  This may be true if you are really high up there (GM, VP, Coach, etc.) - but for most of us - it's enough to make a decent living.  To reiterate Ben's comments, I have frequently heard the quote "30 under 30".  This means that most organizations hire analysts under 30 years old and make less than $30k a year!  Add on the fact that you will work more hours than you ever expected - but if this is what you love, then none of this should matter!

2. Technical skills are becoming more and more important as we enter a data age that we have never experienced before.  One example, NBA's SportVu is a data monster that teams are interested in but need folks that understand programming, statistics, visualization and much more.  If I were to tell you that a single game could contain up to a gigabyte of information, you should be prepared to not even blink at that comment!  Knowing what to do and how to do it will be your greatest weapon when fighting for a job in sports analytics!

3.   This comment is true for any job you apply for, but Ben makes a great comment: "These folks are very smart and know their sport much better than you do - I promise".  This is so true and needs to be kept in mind when interviewing.  Don't try and tell them you know more or know better than them, simply listen and add input about where you could add value!  Sports organizations typically like how they do things - offer ways that you are valuable in the sense that you can enhance their current capabilities without stepping too far outside the box.  Telling them they need to completely rewrite things, reorganize or anything similar - probably not the best approach...

4.  Do something!  This is extremely important and I have firsthand felt the consequences of not doing enough of this.  Professional (and collegiate) organizations actively search the internet to see who is doing what out there.  If they like what they see, they will often contact the authors and explore more.  Many folks in the industry have landed jobs in this fashion (without even applying).  I have talked with several NBA front office staff and they confirm this.  I even interviewed for an MLB organization and quickly realized why I didn't get the job - sure, I may have been more qualified from a professional and educational perspective - but the new hire had been writing baseball blogs for years with unique statistical insights.  So learn from me - get out there and try new things!

Anyways, thanks to Ben for writing his blog entry - I wanted to add more to it to hopefully reach to many more folks out there!  Don't be discouraged - but rather - be well informed and best of luck!

Monday, June 17, 2013

Unsolved Mysteries: NBA Edition #10

The final question in Kevin Pelton's 10 questions to solve in the NBA is finally here!

Question 10:  How do we best predict the outcome of games or series?

We have relished the question and hopefully you have been following our website to see us actively publish our daily predictions in the NBA (winner and spread).  Many people ask how we do our predictions and maintain such high accuracies.  A few years ago I published an article that explains in high level detail how I do predictions in the NBA.  Since then, we have maintained a similar approach to predicting the winner of every game. 


First, we try to keep things simple.  It really comes down to how teams matchup -- through their basic stats.  The better shooting team will typically win games.  Combine that aspect with strong defense and you are almost a sure thing.  Typically, there are no surprises in the NBA -- standings are usually the same year after year (unless trades or injuries occur).  Was anyone surprised that Miami and OKC finished at the top?  Are we shocked to see the Spurs in the finals (after Westbrook went down)?  Although the Bobcats tried to mislead us at 7-5, did they not finish where they were supposed to?

Keeping to the idea of being simple, a lot can be explained on why a team wins through their box scores.  It takes a little craftiness to determine which box scores to use (home/away, last few games, vs conference, vs division, etc.).  Another aspect is to determine which statistics are important.  This can be accomplished through feature reduction by techniques such as principal component analysis or factor analysis.  Finally, a strong prediction algorithm (such as artificial neural networks) can take a seemingly impossible problem and make is simple. 

So, in short, I don't think this is a problem to solve.  We are doing it every day and have maintained an 81% accuracy in predicting the winner of every NBA game this season.  Most of our incorrect decisions came at the beginning (not enough data) of the season and at the end (injuries, rest) of the season.  Are there better stats out there to use -- maybe -- but why make a problem more difficult than necessary?

This concludes our examination into the 10 questions to solve.  We will start posting some work we are currently doing and information related to other sports.  Will the Spurs win in 6 -- our model says no...

Unsolved Mysteries: NBA Edition #9

We have reached question 9 in Kevin Pelton's 10 questions to solve in the NBA

Question 9:  What is the market value for player performance?

Obviously this question is asked a lot especially when people wonder why athletes are paid millions of dollars to play a sport.  What warrants a high salary?  Is Lebron James really worth $50 million like he thinks (or $40 million according to Pelton)?  Are a lot of players overpaid?


I like Pelton's use of WARP (wins above replacement metric), but I don't think this necessarily encompasses all what a player is worth.  In addition to the number of wins a player can provide a team, there are the other aspects of revenue management that need to be included.  Such things are the ability to generate ticket sales, sponsorship opportunities (as we will start to see on backboards next year) and other revenue-generating circumstances. 

Are players paid what they are worth -- I think it really comes to a comparison to other players.  It's not the dollar amount that is of interest (although a GM or owner might say otherwise), but rather it is how much a player makes compared to others.  Lebron should have the highest salary in basketball, but chose not to in order to help form the Big 3.  Therefore, does it really matter if players are paid what they are worth (Chris Paul and Dwight Howard in 2013)?  It only really matters that players aren't overpaid (Kobe anyone?) and organizations aren't "losing" money on their supposed stars.  

If it came down to player performance through various statistics (personal and +/-) and WARP, sign me up!  Otherwise (and unfortunately) it seems to come down to perception, mania (Lin and Terry), agent skills and greed that drive salaries up and down -- and pure stats lose the battle here... 

Thursday, June 13, 2013

Unsolved Mysteries: NBA Edition #8

We are continuing on with Kevin Pelton's 10 questions to solve in the NBA

Question 8:  How do statistics translate from other leagues to the NBA?

Luck would have it we are looking at this exact problem.  We have been asked in the past if one could determine which players in the European leagues would do well in the NBA.  However, we are currently examining which players at the collegiate level will do well in the NBA.  I cannot yet get into the specifics, but we are able to determine with high accuracy which players fit into specified categories (level of NBA talent) based solely on box score data in college.

Pelton discusses the use of translations to determine how college players will do in the NBA.  He notes that while this is successfully used in baseball, the ability to translate statistics is much more difficult.  We are taking a somewhat different look at the problem. 

Every NBA player has been categorized based on their talent/success in the NBA.  This could be by statistics, influence or any number of subjective measures.  We then break these groups up by positions so that we do not measure centers the same as point guards (Pelton did the same).  Each position possesses their own unique set of statistics that explain each player (common techniques such as principal component analysis or factor analysis can be applied here). 

After capturing the right statistics for each group, we can then apply various prediction algorithms to determine which players belong in which groups.  The beauty behind our algorithms is that they "see" things we could easily have missed!

I look forward to sharing our results.  With this information, we take the guessing out of the draft and provide insight into the true floppers of the NBA. 

A fantastic question by Kevin Pelton -- which should be number 1 on the list.  If you claim to love Moneyball, this question should just scream potential to you! 

 

Unsolved Mysteries: NBA Edition #7

It seems only fitting that we examine question 7 of Kevin Pelton's 10 questions.

Question 7:  What role does coaching play in the success of teams and players?

The entire NBA nation might say a tremendous role since the Spurs' Popovich has shut down Lebron James with his "I dare you to shoot jumpers" defense.  So far, the King has been dethroned. 

Very little analytical work has been conducted on coaches, as pointed out by Pelton.  Coach tenures are very short in the NBA with the exception of several notables.  Next to Popovich (1996) and Doc Rivers (2004), the longest tenured coaches in the NBA are Spoelstra, Carlisle and Brooks since 2008.  Each of these coaches have made the NBA Finals. 

This fact alone shows some merit that the success of teams may rely on coaching.  However, are the players growing?  Are they getting better under direction?  Or do these teams just have good players?

Analytically, I do not see the value in pursuing this question.  Every coach approaches the game differently and every player accepts coaching advice in a different manner.  The key is finding the right coach that fits the system in place (Spoelstra is a decent candidate) or building a system around the right coach (Doc and Popovich). 

Friday, May 31, 2013

Unsolved Mysteries: NBA Edition #6

We have been MIA too long!  The NBA playoffs have proven to have its ups and downs but the latest Miami/Indiana saga has been exciting to watch. 

In the spirit of the playoffs, we are going to finish up with Kevin Pelton's 10 questions to solve. 

Question 6: Do per-minute (or per-play) stats translate across changes in playing time?

In my opinion, this is one of the most exciting statistical questions in this list.  Each year we see a new 6th man of the year -- it seems that with more playing time they seem to do better and better (with the exception of JR Smith in the playoffs).  Lance Stephenson has taken the bull by the horns with his increased minutes this year. 

Recently we did a study on a particular Houston Rockets player to measure his per minute stats throughout a game.  Ideally, this player was hoping for more playing time based on particular statistics.  An important aspect was rebounds per minute.  The chart below shows that Houston had more rebounds per minute when this player was off (red line) the court versus when he was on (blue line) the court. 


However, if we put our tunnel vision on, we may fall in to the trap of saying that this player is detrimental to the team in terms of rebounding the ball.  When examining other metrics, we find that Houston has far fewer rebound opportunities when the player is on the court versus off -- thus leading to fewer rebounds per minute.  In addition, field goal attempts were much smaller was well.  Two words - Good Defense. 

Could this type of stat analysis lead to determining more playing for players?  I think so -- it steps outside the bounds of how we normally look at per minute stats.  It would be great to see more research done in this area -- especially its effects come playoff time!