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Viewing: Blog Posts Tagged with: Ben Shields, Most Recent at Top [Help]
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1. Is it really over for RG3? It’s too soon to tell.

In a recent article for Huffington Post, numberFire.com CEO Nik Bonaddio stated: “RG3: It’s Over”. Bonaddio is asserting that it is unlikely that Washington Redskins quarterback Robert Griffin III (RG3) will ever be able to return to the form that enabled him to win the 2012 Rookie of the Year Award and made him one of the best quarterbacks in the NFL that year. More specifically, Bonaddio claims, “The numbers are quite clear: no quarterback who suffered that bad of a precipitous fall in performance ever recovered.”

While Bonaddio may end up being proven correct, there are several problems with his analysis. More specifically, the numbers are not clear at all. Bonaddio starts with using his company’s Net Expected Points (NEP) metric that examines how many points a player’s team should score given his performance. He then states that only six QBs have ever had a drop in their NEP similar to RG3’s drop after his 2012 season. They are: Steve Beuerlein in 1999, Elvis Grbac in 2000, Jay Fiedler in 2001, Tommy Maddox in 2002, Derek Anderson in 2007, and David Garrard in 2007.

As far can be discerned from the information in the article, these are the only quarterbacks used by Bonaddio in his analysis. Having a sample size of six is a very small number to use in such an analysis. Furthermore, even this small sample has major problems when making a comparison to RG3. They are:

  • The average age for when each of the quarterbacks in the sample had their best years is 29.8 years old while RG3 was only 22 years old during his rookie year. Only Anderson, at age 24, was close to the age of RG3 in 2012 during his best season.
  • Each of the quarterbacks had played in the NFL for at least one year before having their best season. Only Anderson had played in one season before his best year. The rest of the quarterbacks had played in multiple years before having their best years.
  • None of the quarterbacks had a significant injury that could account for the subsequent decline in their performance.
  • Each of the other quarterbacks had little mobility. RG3’s success, as stated by Bonaddio, is predicated on his ability to run the football.
  • None of these quarterbacks was a Heisman Trophy winner or had the same level of success as RG3 did in college.

At the end of the article, Bonaddio claims “Regardless of what the cause was [of the decline], the effect is obvious and it’s rather tragic.” The cause of RG3’s decline is extremely important, especially compared with these other six quarterbacks. For example, it is impossible to rule out that these six quarterbacks were never good, or at least as good as RG3. They had one good season in the midst of having many relatively mediocre or poor seasons.

In addition, an NFL quarterback’s prime is age 29 with his prime range being 26-30 according to Football Perspective. Again, the average age of the six quarterbacks is 29.8. Given his college and 2012 performances, RG3 could just be a more talented quarterback than any of these other players. Since he has not reached the prime age range in his career, RG3 could also continue to improve as he gets older and gains more experience.

There is also a clear reason for RG3’s decline: his injury history. Bonaddio does point out that RG3 did suffer significant injuries both during the 2012 and 2014 seasons that caused his NEP to decline. However, he does not fully account for the fact that RG3’s poor performances in 2013 and 2014 could be due to injuries and recovery from injuries rather than a decline in his skillset. It is definitely possible that RG3 may never recover his running ability from 2012 or that he is more injury prone than other NFL quarterbacks. However, it is impossible to know what RG3’s best performances can be until that can be proved to be the case or he has actually had played in more games where he has fully recovered from these injuries.

Bonaddio’s analysis does show the potential problem with working with advanced analytics in sports. The NEP could be a valuable metric that provides great insights about the true performance of quarterbacks. However, making assertions beyond the stated use of the metric that rely on small sample sizes with clear confounding variables can lead to problematic conclusions. It may or may not be over for RG3, but it is impossible to tell using the evidence presented in Bonaddio’s article.

Featured image credit: Robert Griffin III on a read-option run during the Redskins 24-16 loss to the Eagles in the 2013 season. Photo by Mr.schultz. CC BY-SA 3.0 via Wikimedia Commons.

The post Is it really over for RG3? It’s too soon to tell. appeared first on OUPblog.

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2. Analyzing the advancement of sports analytics

The biggest story heading into the 2014-15 National Hockey League (NHL) season appears to not be what is happening with players on the ice. Rather, it is the people working off the ice who evaluate players’ performance on the ice that have a leading role in the NHL’s narrative. The analytics movement has come full force to professional hockey. Teams all across the NHL have hired people with expertise in analytics that can develop proprietary statistical analysis to give their teams a competitive edge. The Toronto Maple Leafs alone hired three analysts this past offseason.

The NHL is the latest league to make a significant investment in analytics. Major League Baseball (MLB) is well-known for its use of sabermetrics, as most famously deployed by general manager Billy Beane and the Oakland Athletics. The National Basketball Association (NBA) has spent the last decade hiring people for senior level positions with strong analytics backgrounds, as exemplified by the Houston Rockets selecting Daryl Morey for their General Manager role.

The rise of analytics to evaluate player performance raises a natural question. If teams and leagues increasingly believe analytics can provide a competitive advantage during competitions, then why not make more use of use analytics to help their businesses as well? In fact, sports teams and leagues should take advantage of the opportunities to hire quantitatively-savvy managers and analysts focused primarily on growing an organization’s revenue.

What value does this provide to an organization? In a recent article in Forbes, I showed how combining analysis of a quarterback’s on-field and off-field performance can provide a more holistic view of his value to an organization. However, focusing on individual athletes’ economic impacts is only the start of how quantitative analysis can impact sports organizations’ businesses. The most common example is with pricing tickets. Dynamic pricing has changed the way that teams, fans, media, and sponsors think about how they purchase tickets. Secondary market ticket sites, such as StubHub, and new dynamic ticket pricing models, such as Purple Pricing, have provided sports organizations with the opportunity to make more money while giving fans better options for buying tickets to games.

Ticket pricing is not the only revenue stream where analytics can be applied. For example, sponsorship revenue can use a more analytical approach to demonstrate how sports organizations often generate a significant return on investment for their partners. Sports organizations have traditionally used qualitative approaches to demonstrate a return on investment for their corporate partners in sponsorship deals. This includes developing recaps that have pictures of sponsorship activation elements during the course of the season such as a picture of a brand’s logo on signage at a sports venue.

However, corporate partners should be presented with a dollar amount for the return on investment that they are receiving by sponsoring an organization. Teams can use analytical models to show how the impressions they generate with lucrative sports audiences creates new customers, helps retain current customers, increases brand awareness, or enhances brand perception. Employing analytics in sports sponsorship provides sponsors with clear reasons why they are getting value by working with a sports organization.

Employing business analytics also helps to specifically address issues when a team or athlete is not successful in competition. Relying on winning is a losing strategy. Teams that rely on winning do not always achieve financial success. In addition, winning is still difficult to predict or control – even as teams hire more people to analyze their competitive performance. Deploying business analytics helps to address these issues. It can show what strategies, marketing campaigns, and promotions work best to generate revenue regardless of a team’s performance. With the influx of new technology into the sports industry impacting ticket purchases, in-game concession sales, digital and mobile streaming, social media engagement, and many others, there is a wealth of new data available to sports organizations. The next Moneyball will be the teams that can find insights from this data to generate money for their organizations.

Headline image credit: Ice hockey stadium. CC0 via Pixaby.

The post Analyzing the advancement of sports analytics appeared first on OUPblog.

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