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- Hyoun Park - Nucleus Research on Are You Ready for Some Analytics 3.0?
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TIBCO Spotfire's Business Intelligence Blog
Category Archives: Sports Analytics
Perhaps the most visible evidence of the competitive advantage that can be fueled by data analysis is LeBron James’ performance in the NBA playoffs this year and last, compared to previous lackluster post-season play by the Miami Heat superstar.
But King James’ less-than-stellar performance on the basketball court happened before he took a hard look at the analytics behind his play, notes Michael Schrage, a research fellow at MIT Sloan School’s Center for Digital Business, in a post in Harvard Business Review.
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Before finalizing your Fantasy roster for Week 5, @TIBCOSpotfire‘s resident sports analytics geek, @Brett2point0, has a few last minute data-driven insights for head-to-head, “start ‘em/sit ‘em” matchups:
#FantasyFootball insight: Chiefs among bottom 11 #NFL defenses, allow back-breaking pass plays>>result in TDs
The data suggests that Kansas City’s glaring weakness is in the secondary. Good news for owners of Baltimore’s deep threat, Torrey Smith, and quarterback Joe Flacco!
Given the star rookie’s start, this is a bit of a no-brainer. Though Atlanta is among the top 15 defenses in the league, Washington should have success on the ground as Atlanta allows an average of 147 rushing yards per game (as well as 4+ yards per carry).
So, good luck, and after you bring home a “W” against your friend, work colleague, or crazy uncle . . .
- Begin discovering insights in your own data: Try Spotfire, free evaluation.
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When Houston gets down to the second half, the team will turn to both Arian Foster and Ben Tate and the ground game to protect their lead on Tennessee, kill the clock, control the ball, and escape with an easy victory. Grab those “garbage time” points!
Avoid #FantasyFootball match-ups w/NFC West teams, toughest #NFL division to score against in 2012: #dataviz #analytics
If you can avoid it in a choice between players in a flex roster spot, avoid players matching up against NFC West teams whenever possible (especially San Francisco, Seattle, and Arizona). It can be a very, very long Sunday afternoon when your Fantasy players’ offense isn’t even on the field.
#FantasyFootball insight of the week: bench “Action” Jackson vs SEA (tough against the run): https://bitly.com/P9I5Pt #NFL #dataviz #analytics
With that said about Seattle in the NFC West, one player facing a tough road to production this week is St. Louis running back Steven “Action” Jackson. If you have better options, bench him.
The world of big data analytics first enters the public mainstream with the popular book and hit film Moneyball, which chronicles the Oakland A’s success using statistics and data mining to win Major League Baseball games.
Many of Oakland’s MLB competitors have started playing Moneyball, as have a bevy of National Basketball Association teams. Recently, English soccer teams began turning to analytics to boost their performance on the field.
While so much of today’s game has been boosted by technological advances – such as film being replaced by digital video and game planning that can be done via software – many teams have not fully embraced the power of analytics to enhance their scouting, Sundquist argues.
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England’s Manchester City Football Club (soccer to those of us in the US) is taking the Moneyball statistical analytics concept made famous by the Oakland A’s baseball team a step further, by releasing detailed data about the team to the public to try to mirror the success the NBA and MLB have had in the US.
“There are many people in the analytics community right now who have the skills, desire and vision to make a difference in the performance analytics space, people who can add significant value such as Bill James did in baseball,” team officials note when announcing the move. “But those people have no significant data to work with.”
While this move is “essentially unprecedented in the soccer world,” it follows the open-source and crowdsourcing trend of making data open to large groups of people to spur innovation, notes the Atlantic magazine.
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I am a sports fanatic (although I draw the line at professional wrestling) and as such, I can’t wait for the kick off to the London 2012 Summer Olympics.
And that got me to thinking about what we could learn from all the data associated with the Olympics. So I did a little research and found the really terrific official Olympic site and I thought I’d share some of the more interesting Olympics factoids from that site and others with you.
Did you know that London will be the first city ever to have hosted three Olympics? The city was a last-minute choice in 1908, stepping in for Rome after Mount Vesuvius blew its top. Then London got the nod again in 1948 when Germany and Japan were banned after they lost World War II.
And here’s an infographic with some interesting Olympic info:
The cheapest tickets in Athens in 2004 were £43 ($67), more than double London 2012’s bargain price of £20.12 ($31) for certain events including the opening ceremony, according to The Telegraph in the UK. But at the other end of the spectrum the most expensive tickets to events at the 2004 Olympics in Athens cost a maximum of £833 ($1,298), much less than the $7,300 (£4,684) per person price tag (before taxes) to some of the events in London.
The UK Anti-Doping association has released figures on drug testing for athletes from 26 of the sports participating at the 2012 London Summer Olympics. The statistics, complied by the World Anti-Doping Agency, include the number of samples tested in each sport for every year between 2003 and 2010, as well as the number of tests that indicate the use of a prohibited substance.
Between 2003 and 2008 the total number of samples taken across all sports increases from just over 100,000 to 166,185. In 2009 this figure falls slightly to 166,106, and the most recent total, taken in 2010, stands at 162,130 .
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Today is a big day for all you football fans and data geeks. Are you ready for the NFL draft?
Tonight, after nearly not having a 2011 season, we’ll see NFL teams lessen their risks as new NFL collective bargaining rules go into effect. According to an article in Forbes, this year’s draft is less of a risk than years past and “drastically” reduces the amount of moolah “an unproven player” can receive in his first year.
From a business perspective, it’s a good move to make a decision based on analytics and performance over the perceived value of talent. And some teams fare better than others when you look at the historical data.
To determine which teams have scored touchdowns in past drafts and which teams couldn’t even split the uprights, Forbes turned to a source after our own hearts – the analytics of drafted players over the course of four years (2005 to 2009).
The author defined parameters and specific questions before engaging the data and was able to make some solid decisions on which teams have made the best and worst NFL draft picks over time.
Best & Worst Teams in Draft Proficiency
We won’t leave you in suspense for the best and worst teams regarding drafting proficiency (according to research conducted by the author, Patrick Rishe, and a Syracuse sophomore with a bright career ahead – perhaps in analytics – named Tyler Wasserman).
According to Rishe’s feature in Forbes, the best teams at draft proficiency are the Green Bay Packers and the New York Jets and the worst teams at draft proficiency are the Cincinnati Bengals and the St. Louis Rams.
Now, for the data analytics criteria:
- The researchers looked at a strict time period – 2005 to 2009 – to ensure that the players had sufficient time in the league to adapt to the pro environment;
- They analyzed seven rounds of draft picks for 32 teams. (That’s all of the teams and all of the rounds.);
- They looked at four variables to determine draft proficiency: the percentage of games a draft pick played; the percentage of games the player started; the career of a player (still playing in the NFL); and the number of Pro Bowl nods a player earned.
With this criteria, Rishe calculated an index that he explains here to get his top picks for draft proficiency. It’s well worth the read.
Bad Draft Pick = Costly Decision?
According to our featured infographic, the impact of a bad hiring decision in football can result in the loss of millions of dollars. However, a traditional college grad who works from age 25 to age 64 earns about $2.1 million or roughly $51,000 per year. That’s just 1% of an NFL draft flop. But here’s a question for you to ponder: Would a $51,000 flop hurt your business? Analytics can help any HR executive come up with the right answer.
The Year of the Quarterback Draft
Who will be the lucky first named pick – the appropriately named Andrew Luck (Stanford) or Heisman Trophy winner RG3 (Robert Griffin III of Baylor)? The expectation is that Luck will get the draw and take Peyton Manning’s old seat at the head of the Indianpolis Colts and RG3 will go second to the Washington Redskins. But strange things can happen in the NFL draft.
An interesting note about this year’s draft is that it’s only the fourth time since the NFC and AFC combined their selection processes in 1967 that two quarterbacks will go first and second. Even more interesting – in each of the other three times two quarterbacks went first and second in the draft, only one of the two went on to have a successful career in the league. Maybe RG3 and Luck will change this stat.
Defensive Deficiency Draft Indicator
Just for fun and to give the data geeks a little context around the defensive side of the draft, we’ve put together a heat map visualization on the 2011 season defenses. It breaks down the most important team defense KPIs and ranks each team by yards per rush, yards per pass, total yards allowed and total points allowed. It could give you a few insights into NFL draft strategy based on the defensive deficiencies. For instance a team that has high yards per rush allowed may need to draft interior linemen and middle linebackers and teams with high pass yards allowed may need a little help in the pass rusher and corner back positions.
- Don’t forget to check out our complimentary “5-Minute Guide to HR Analytics“ to see how you can get solid answers to questions like: “How do we spot and retain the most valuable players?”
Spotfire Blogging Team
When Bubba Watson made par on the second hole of a sudden-death playoff to defeat Louis Oosthuizen of South Africa at The Masters Golf Tournament on Sunday, he wasn’t the odds-on-favorite to win.
However, he was among the top seven golfers in the event picked by Las Vegas oddsmakers to win the tournament at 30-to-1. Tiger Woods was the odds-on favorite among Las Vegas bookmakers to win this year’s Masters at 4-to-1. Irish phenom Rory McIlroy was next at 5-to-1 odds, followed by Phil Mickelson and Lee Westwood at 10-to-1 and 18-to-1, respectively. Oosthuizen, the runner-up, was given 100-to-1 odds to win.
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OK, all you data geeks and baseball fans . . . “Moneyball“ has been on DVD and Blue Ray for a few weeks. How many times have you watched it in anticipation of April 4? You know, Opening Day for Major League Baseball and the start of a very special season for the Boston Red Sox, who will celebrate the 100th anniversary of Fenway Park on April 20.
And we expect the analytics discussion to have a banner year in the field of baseball, especially in the aftermath of “Moneyball’s” popularity.
Focusing on Opportunities & Outcomes = More Wins
“Moneyball,” the story of the Oakland Athletics General Manager Billy Beane, who pioneered the use of player statistics and analytics to scout players, maximize payroll, and utilize talent that was often overlooked or undervalued, shows the value of an analytics plan in action.
Beane worked with a lean budget (like so many of us in technology and business) that was dwarfed by the budgets of big-market teams such as the New York Yankees and the Red Sox. His story is a helpful reminder that the competitive landscape doesn’t always favor the big players anymore. Star power doesn’t decide who wins the game. There are many more factors to consider.
Predictive analytics can be used for a wide variety of applications, including matching the right offer to the right customer at the right time using data from customer transaction history, customer needs and preferences, as well as customer lifecycle status.
Last week, many data scientists and other college basketball enthusiasts found themselves making extensive use of statistics and analytics tools for altogether different purposes: to gain an edge on fellow March Madness bettors in their efforts to make correct picks in their NCAA tournament pools.
Even with the help of analytics, making the right picks isn’t easy: there are an astounding 9.2 quintillion possibilities for the possible winners in a 64-team bracket in the NCAA tournament. By expanding the field to 68 teams in 2011, the odds of picking all of the bracket winners has increased to 147.57 quintillion to 1.
One approach to picking the winners involves the use of analytics tools to help determine the statistical likelihood of certain tournament seeds beating other types of seeds (e.g. #13 versus #4). GigaOM and BusinessWeek blogger Derrick Harris notes that he makes his picks using a tool called BracketOdds created by University of Illinois computer science professor Sheldon Jacobson.
BracketOdds lets you know the probability of any combination of seeds making it to a given round in the tournament. For the Final Four, the most likely seed combination this year is 1,1,2,3. The odds against this combination occurring is only 16.08 to 1. As Harris notes in his blog, the odds of each of the top seeded teams making the Final Four is 48.7 to 1, so the chances of a #2 seed and a #3 seed making the mix is three times more likely to occur.
Of course, sentimentality and bias often factor into the NCAA picks made by data scientists and other gamers. One analyst I spoke to this week says he includes a range of variables with his team selections, such as a comparison of team records, the strength of each team’s regular season schedule versus its opponent, game location and the proximity to each school’s campus/fan base, the statistical likelihood of “sleeper” teams (e.g. overlooked 11th seeds) to advance in specific rounds of the tournament, etc.
But when I pressed him on which team he picked to win, he confessed his partiality for the University of Missouri. A #2 seed in the West region, Mizzou is known for its guard tandem. And it’s his alma mater.
But in the end, Norfolk State became just the fifth #15 seed in tournament history to knock out a #2 seed (Mizzou), shortly followed by Lehigh’s upset win against #2 Duke. Statistically unlikely to occur? Absolutely.
The odds of a #15 seed beating a #2 seed are 25 to 1. Finding this type of information isn’t always easy. Decision makers in business sometimes encounter similar challenges. Indeed, it’s critical for business leaders to be able to get at the type of information they need when they need it.
So how did Norfolk State pull off its historic upset? In part, by scoring a highly efficient 1.34 points per possession.
Cinderella teams have pulled off upsets in previous tournaments but they’ve rarely advanced into the final rounds. In fact, in the history of the tournament, only a small number of teams seeded lower than #8 has actually made it to the final rounds. Just two #14 seeds have reached the Sweet Sixteen (Cleveland State in 1986 and Chattanooga in 1997).
Meanwhile, a #12 seed has made it to the Elite Eight just once (Missouri in 2002) while an #11 seed has reached the Final Four just three times (LSU in 1986, George Mason in 2006 and Virginia Commonwealth in 2011). And in perhaps the greatest upset in championship game history, the #8 seeded Villanova Wildcats stunned #1 seed Georgetown 66-64 on April 1, 1985.
Improbable, but it can happen.
- Sign up for our April 5 complimentary webcast: Make Better Decisions with Predictive Analytics in Spotfire with Lou Bajuk-Yorgan (@LouBajuk), Tibco’s Sr. Director, Product Management.
- Download our complimentary 5-Minute Guide to Business Analytics and learn how analytics technologies can help you uncover the most relevant data when you need it.