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Kawhi Leonard’s impact on the Toronto Raptors’ 2019 playoff run as a Markov chain

dc.contributor.authorLupul, Nicholas
dc.date.accessioned2022-02-25
dc.date.accessioned2022-05-31T01:50:51Z
dc.date.available2022-05-31T01:50:51Z
dc.date.issued2020
dc.description.abstractIn the summer of 2018, the Toronto Raptors engineered a trade that would forever change the history of their franchise. The blockbuster trade saw NBA superstar Kawhi Leonard in a Raptors uniform in exchange for then franchise cornerstone DeMar Derozan. The trade was heavily criticized with fans and analysts alike claiming the organization gave up its future for a small chance at a championship. The Raptors went on to win the championship with Kawhi as their centerpiece. By studying their performance in the playoffs as two separate Markov chains, when Kawhi was playing and when he was resting, his contribution can be analyzed. It was assumed that his presence would account for more defensive stops and a more efficient offense. Upon analyzing the collected data, it was seen that his presence accounts for more points per game and offensive rebounds per game and a decreased number of defensive stops. In the future this type of analysis can be applied to data from any team at any level where relevant statistics are tracked. By analyzing one player’s impact on games, organizations will have a better idea of which players to trade away or trade for as well as how to distribute minutes.
dc.format.extent904.09KB
dc.format.mimetypePDF
dc.identifier.citationLupul, N. (2020). Kawhi Leonard’s Impact on the Toronto Raptors’ 2019 Playoff Run as a Markov Chain. MacEwan University Student EJournal (MUSe), 4(1). https://doi.org/10.31542/muse.v4i1.1868
dc.identifier.doihttps://doi.org/10.31542/muse.v4i1.1868
dc.identifier.urihttps://hdl.handle.net/20.500.14078/2590
dc.languageEnglish
dc.language.isoen
dc.rightsAttribution-NonCommercial (CC BY-NC)
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.titleKawhi Leonard’s impact on the Toronto Raptors’ 2019 playoff run as a Markov chainen
dc.typeStudent Article

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