TSG Hoffenheim: Football in the Age of Analytics

Feng  Zhu; Karim R. Lakhani; Sascha L. Schmidt; Kerry  Herman

Case | HBS Case Collection | August 2015 (Revised May 2017)

TSG Hoffenheim: Football in the Age of Analytics

by Feng Zhu, Karim R. Lakhani, Sascha L. Schmidt and Kerry Herman

Abstract

In 2015, Dietmar Hopp, owner of Germany's Bundesliga football team TSG Hoffenheim and co-founder of the global enterprise software company SAP, was considering how to ensure long-term sustainability and competitiveness for TSG Hoffenheim. While historically a small team from bottom rungs of the league, TSG Hoffenheim, with revenues of €60 million to €70 million, reached the top division of the Bundesliga in the 2008–2009 season thanks to a deliberate strategy focused on enhanced scouting, strong youth programs, and innovative technology and analytics that improved player development. In 2014 Hopp, who had personally invested €300 million in the club, built a "footbonaut," an automated training environment that collected data on players' skills and strengths. The tool, one of three in the world, helped scouts and coaches better assess and develop each player. Yet some managers felt the technology was a distraction, an investment too expensive for a team that was not yet cash-flow positive. The team finished the 2014–2015 season in eighth place, below the top division, and Hopp wondered whether the focus on technology and analytics was the right strategy to grow the club. He wondered if the "moneyball" approach—when a smaller team competed with wealthier teams by using statistical analysis to buy undervalued assets and sell overvalued assets—could work in football and if investments in technology could lead the team to financial independence.

Keywords: Technology; Measurement and Metrics; Sports; Sports Industry;

Language: English

Format: Print

35 pages

Educators Purchase

Citation:

Zhu, Feng, Karim R. Lakhani, Sascha L. Schmidt, and Kerry Herman. "TSG Hoffenheim: Football in the Age of Analytics." Harvard Business School Case 616-010, August 2015. (Revised May 2017.)

In summer 2016, four of Australia’s top five banks petitioned regulators for permission to bargain collectively with Apple over the terms under which they would support its digital wallet, Apple Pay. They argued that doing so would force concessions from Apple that would improve market competitiveness, consumer choice, price transparency, and transaction security for all mobile payments in Australia. The banks stood to benefit as well. If they succeeded in their primary aim of opening the contactless payment functionality of Apple’s devices to their own digital wallets—something Apple had thus far refused in any market it had entered—the banks could retain customer relationships that Apple Pay threatened to usurp and limit Apple’s free-riding on the contactless payment infrastructure the banks had just spent several years building.

Article | MIS Quarterly | September 2018

Do Experts or Crowd-Based Models Produce More Bias? Evidence from Encyclopædia Britannica and Wikipedia

Shane Greenstein and Feng Zhu

Organizations today can use both crowds and experts to produce knowledge. While prior work compares the accuracy of crowd-produced and expert-produced knowledge, we compare bias in these two models in the context of contested knowledge, which involves subjective, unverifiable, or controversial information. Using data from Encyclopædia Britannica, authored by experts, and Wikipedia, an encyclopedia produced by an online community, we compare the slant and bias of pairs of articles on identical topics of U.S. politics. Our slant measure is less (more) than zero when an article leans towards Democratic (Republican) viewpoints, while bias is the absolute value of the slant. We find that Wikipedia articles are more slanted towards Democratic views than are Britannica articles, as well as more biased. The difference in bias between a pair of articles decreases with more revisions. The bias on a per word basis hardly differs between the sources because Wikipedia articles tend to be longer than Britannica articles. These results highlight the pros and cons of each knowledge production model, help identify the scope of the empirical generalization of prior studies comparing the information quality of the two production models, and offer implications for organizations managing crowd-based knowledge production.

Keywords: online community; Collective Intelligence; wisdom of crowds; bias; Wikipedia; Britannica; knowledge production; Knowledge Sharing; Knowledge Dissemination; Prejudice and Bias;