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For a long time a football match has been considered impossible to model or predict due to the high amount of variables that come into play: There are 22 players on the field, 3 referees, coaching and training staff, fans, numerous amounts of formations and tactics, emotions, luck and randomness (Evans, 2019). However the amount of money that has been attracted by the football industry has increased the professionalization of clubs, creating a vast ecosystem (Dörr, 2019). With this ecosystem growth, data analytics has become an emerging asset in the sport with more and more teams successfully making use of advanced technologies. Areas impacted the most through sport analytics are training, injury prevention, match-analysisand scouting.

Through wearables such as the Catapult Sports’ vest, up to 1.000 data points per second can be collected of a player during training. Every shot, sprint, dribble, pass or challenge is being recorded and analyzed (Evans, 2019). This training data can be combined with the tracking of a players sleep and nutrition routines, to establish personalized training sessions for each player. Not only can the use of data collection during training improve a player’s performance, but it can also be used to forecast injuryrisks. GPS devices collect body movement and metabolic data during training (Dörr, 2019). This data can then be combined with historic injury data of the player and an evaluation can be made if the training intensity for the specific player should be in- or decreased. The data documented through these multiple wearables is stored on cloud-based platforms and can be accessed by any important stakeholder (e. g. coaching and training staff) independent of place and time (Evans, 2019).

Data analysis not only helps to increase the performance of players on the training pitch but also impact how matchesthemselves can be analyzedand prepared for. Machine-learning algorithms are collecting data in order to learn and predict how a certain team responds to different scenarios (Thompson, 2019). Such techniques are called “ghosting” and enable a coaching staff to predict and identify scenarios in which the opponent might be extremely vulnerable for a certain time window (Burn-Murdoch, 2018). In preparation for the match, this information can then be combined with the training data of the player to specifically prepare for these scenarios.  Ghosting techniques are also valuable in the field of talent scouting. Playing styles and the potential player fit into the team to ensure overall compatibility can all be assessed through this data (Burn-Murdoch, 2018).  Besides from ghosting, the creation of various scouting platforms collecting vast amount of statistics, visualizations and videos about players facilitates the process of finding undervalued and/or high potential players (Dörr, 2019).

These few examples are a testament to the fact that sport analytics is becoming progressively important in the world of football and we are already seeing clubs like Liverpool FC and Manchester City reaping the fruits of this technology emergence. In the future having a competitive advantage will not only depend on the skills and performance of players and coaches, but also on the data-gathering, how this data can be stored and transformed into valuable information and on having the ability to interpret these insights correctly.

 

References

Burn-Murdoch, J., 2018. How data analysis helps football clubs make better signings. Financial Times. Available at: https://www.ft.com/content/84aa8b5e-c1a9-11e8-84cd-9e601db069b8 [Accessed October 6, 2019].

Dörr, A., 2019. How data analytics is fast becoming football’s must-have signing. Exasol. Available at: https://www.exasol.com/en/blog/how-data-analytics-is-fast-becoming-footballs-must-have-signing/ [Accessed October 6, 2019].

Evans, D., 2019. Big Data Analytics And The Future of Football. Intel. Available at: https://www.intel.co.uk/content/www/uk/en/it-management/cloud-analytic-hub/data-powered-football.html [Accessed October 6, 2019].

Thompson, M., 2018. Statistical modelling, artificial intelligence and particle physics: What will football of the future look like? The Set Pieces. Available at: https://thesetpieces.com/features/statistical-modelling-artificial-intelligence-and-particle-physics-what-does-the-future-of-football-hold/ [Accessed October 6, 2019].

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The insurance industry at its core is built on incomplete information, where Insurers group subjects with similar risks together and demand a flat premium calculated through the probabilities of covering the liabilities of such a subject group. Even though digital innovation is beginning to close this information gap, for many years incumbents of the insurance sector were able to withstand the digital disruption wave through regulations, high capital requirements and entry barriers (e. g. historical data & long contracts) (Nicolás, 2019). Nowadays, due to ever-growing customer demand for “new needs, greater flexibility, transparency and tailored offerings” (Nicolás, 2019), technological innovations are beginning to stretch the boundaries of the market and changing its dynamics (Catlin et al., 2018), establishing new winners and losers in the insurance industry.

Insuretech companies have been the biggest winners in the industry in recent years as they are offering highly customized policies and dynamical premium pricing (Hargrave, 2019) through the use of the newest technologies in the field of AI, IOT and data analytics (MCKinsey, 2018). One of the main characteristics of these new market entrants is that they mainly focus on one step of the value chain (Product Offerings, Marketing, Underwritingand Claims Management), offering unique and simplified services (Nicolás, 2019). In the Product Offeringsstep companies such as Figo are creating solutions for unserved and underinsured markets, offering “usage or behavior based personalized insurance” (PWC, 2018). Figo Pet Insurance uses its integrated cloud platform to offer personalized healthcare insurance for pets (Smith, 2018). Insurtech firms such as Coverwallet are reinventing the customer experience (Marketing) though online comparison and streamlining and customized customer engagement platforms (PWC, 2018). Underwriting is also being revolutionized through data analytics, specifically in terms of remote data capture and analysis and the quantification of emerging risks (PWC, 2018). Through valuating geospatial data using deep learning and data science Cape Analytics allows customers to better understand the risk profile of their property assets (Cape Analytics, 2019). And in the last step of the Value Chain (Claims Management), robotics and as-a-service platforms are reforming the operation and expense structure (PWC, 2018). CLARA Analytics is using AI to anticipate needs of claimants reducing the cost of claims (CLARA Analytics, 2019).

The rise of Insuretech is threatening legacy incumbents, which traditionally have been vertically integrated in every step of the value chain. Their high operational leverage impedes them to meet the new customer needs as personalized and fast as their Insuretech counterparts (Nicolás, 2019). In order to survive in this changing insurance market incumbents have three strategic options in order to survive. They either both stop their high vertical integration and start focusing on core value chain steps in order to fight off their Insurancetech counterparts, form partnerships or engage in Acquisitions of Insuretech firms in each step of the Value chain they are competing in. If incumbents fail to implement one of these strategies it is only a question of time that they will be driven out of the insurance market.

 

Bibliography

Cape Analytics. (2019). About us. [online] Available at: https://capeanalytics.com/about/ [Accessed 5 Oct. 2019].

Catlin, T., Lorenz, J., Nandan, J., Sharma, S. and Waschtco, A. (2019). Insurance beyond digital: The rise of ecosystems and platforms. [online] McKinsey & Company. Available at: https://www.mckinsey.com/industries/financial-services/our-insights/insurance-beyond-digital-the-rise-of-ecosystems-and-platforms [Accessed 5 Oct. 2019].

CLARA Analytics. (2019). Products. [online] Available at: https://www.claraanalytics.com/products/claims [Accessed 5 Oct. 2019].

Hargrave, M. (2018). Insurtech. [online] Investopedia. Available at: https://www.investopedia.com/terms/i/insurtech.asp [Accessed 5 Oct. 2019].

McKinsey & Company. (2019). Digital insurance in 2018: Driving real impact with digital and analytics. [online] Available at: https://www.mckinsey.com/industries/financial-services/our-insights/digital-insurance-in-2018-driving-real-impact-with-digital-and-analytics [Accessed 5 Oct. 2019].

Nicolás, N. (2019). Digital Disruption in the Insurance Industry; Winning by unlocking the Value Chain – Digital Innovation and Transformation. [online] Digital Innovation and Transformation. Available at: https://digital.hbs.edu/platform-digit/submission/digital-disruption-in-the-insurance-industry-winning-by-unlocking-the-value-chain/ [Accessed 5 Oct. 2019].

PWC. (2018). InsurTech Insights: How InsurTechs are transforming  (re)insurers. [online] Available at: https://www.pwc.ch/en/publications/2018/PwC-%20InsurTech%20Insights%20_%20Final.pdf [Accessed 5 Oct. 2019].

Smith, R. (2018). HCS Capital Deploys $4MM into Growing InsurTech Opportunity, Figo Pet Insurance.[online] Business Wire. Available at: https://www.businesswire.com/news/home/20180314005524/en/ [Accessed 5 Oct. 2019].