{"id":"https://openalex.org/W2187648190","doi":"https://doi.org/10.1109/dsaa.2015.7344823","title":"The harsh rule of the goals: Data-driven performance indicators for football teams","display_name":"The harsh rule of the goals: Data-driven performance indicators for football teams","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2187648190","doi":"https://doi.org/10.1109/dsaa.2015.7344823","mag":"2187648190"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa.2015.7344823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa.2015.7344823","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059674609","display_name":"Paolo Cintia","orcid":"https://orcid.org/0000-0002-8085-9338"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Paolo Cintia","raw_affiliation_strings":["Department of Computer Science, University of Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024505700","display_name":"Fosca Giannotti","orcid":"https://orcid.org/0000-0003-3099-3835"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Fosca Giannotti","raw_affiliation_strings":["Department of Computer Science, University of Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068819495","display_name":"Luca Pappalardo","orcid":"https://orcid.org/0000-0002-1547-6007"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Luca Pappalardo","raw_affiliation_strings":["Department of Computer Science, University of Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044898565","display_name":"Dino Pedreschi","orcid":"https://orcid.org/0000-0003-4801-3225"},"institutions":[{"id":"https://openalex.org/I4210155236","display_name":"National Research Council","ror":"https://ror.org/04zaypm56","country_code":"IT","type":"nonprofit","lineage":["https://openalex.org/I4210155236"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Dino Pedreschi","raw_affiliation_strings":["National Research Council (CNR), Institute of Information Science and Tecnologies, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Research Council (CNR), Institute of Information Science and Tecnologies, Italy","institution_ids":["https://openalex.org/I4210155236"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070929859","display_name":"Marco Malvaldi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210155236","display_name":"National Research Council","ror":"https://ror.org/04zaypm56","country_code":"IT","type":"nonprofit","lineage":["https://openalex.org/I4210155236"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Marco Malvaldi","raw_affiliation_strings":["National Research Council (CNR), Institute of Information Science and Tecnologies, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Research Council (CNR), Institute of Information Science and Tecnologies, Italy","institution_ids":["https://openalex.org/I4210155236"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":88,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11674","display_name":"Sports Analytics and Performance","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11674","display_name":"Sports Analytics and Performance","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10157","display_name":"Sports Performance and Training","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/2732","display_name":"Orthopedics and Sports Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9794999957084656,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/football","display_name":"Football","score":0.8427925109863281},{"id":"https://openalex.org/keywords/championship","display_name":"Championship","score":0.8102164268493652},{"id":"https://openalex.org/keywords/performance-indicator","display_name":"Performance indicator","score":0.7391389608383179},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.72817063331604},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6575908064842224},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6392415761947632},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.606971263885498},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5584596395492554},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.5483511090278625},{"id":"https://openalex.org/keywords/football-team","display_name":"Football team","score":0.5336670875549316},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5020878314971924},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4374277889728546},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.42448121309280396},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4130082130432129},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3134729266166687},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27649667859077454},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.22886601090431213},{"id":"https://openalex.org/keywords/operations-management","display_name":"Operations management","score":0.10982128977775574},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08765846490859985}],"concepts":[{"id":"https://openalex.org/C2778444522","wikidata":"https://www.wikidata.org/wiki/Q1081491","display_name":"Football","level":2,"score":0.8427925109863281},{"id":"https://openalex.org/C2779501167","wikidata":"https://www.wikidata.org/wiki/Q1079023","display_name":"Championship","level":2,"score":0.8102164268493652},{"id":"https://openalex.org/C135510737","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance indicator","level":2,"score":0.7391389608383179},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.72817063331604},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6575908064842224},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6392415761947632},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.606971263885498},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5584596395492554},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.5483511090278625},{"id":"https://openalex.org/C2993400877","wikidata":"https://www.wikidata.org/wiki/Q28083137","display_name":"Football team","level":3,"score":0.5336670875549316},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5020878314971924},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4374277889728546},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.42448121309280396},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4130082130432129},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3134729266166687},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27649667859077454},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.22886601090431213},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.10982128977775574},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08765846490859985},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/dsaa.2015.7344823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa.2015.7344823","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"},{"id":"pmh:oai:arpi.unipi.it:11568/858219","is_oa":false,"landing_page_url":"http://hdl.handle.net/11568/858219","pdf_url":null,"source":{"id":"https://openalex.org/S4377196265","display_name":"CINECA IRIS Institutial research information system (University of Pisa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I108290504","host_organization_name":"University of Pisa","host_organization_lineage":["https://openalex.org/I108290504"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:dnet:people______::8e264bf3ac8bb49713cef9d67727dd73","is_oa":false,"landing_page_url":"https://openportal.isti.cnr.it/doc?id=people______::8e264bf3ac8bb49713cef9d67727dd73","pdf_url":null,"source":{"id":"https://openalex.org/S7407055261","display_name":"ISTI Open Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE International Conference on Data Science and Advanced Analytics, Paris, France, 19-21/10/2015","raw_type":"http://purl.org/coar/resource_type/c_5794"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332999","display_name":"Horizon 2020 Framework Programme","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1518184997","https://openalex.org/W1643201308","https://openalex.org/W1669485619","https://openalex.org/W1693786560","https://openalex.org/W1835587747","https://openalex.org/W1981918053","https://openalex.org/W2025077020","https://openalex.org/W2031229642","https://openalex.org/W2059096325","https://openalex.org/W2091831014","https://openalex.org/W2101234009","https://openalex.org/W2111300734","https://openalex.org/W2145961832","https://openalex.org/W2159533779","https://openalex.org/W2187932643","https://openalex.org/W2403337688","https://openalex.org/W2408095357","https://openalex.org/W2623363880","https://openalex.org/W2746911795","https://openalex.org/W2797002824","https://openalex.org/W2797740415","https://openalex.org/W3125940798","https://openalex.org/W4234034486","https://openalex.org/W4242246215","https://openalex.org/W4254824329","https://openalex.org/W4285719527","https://openalex.org/W6636989463","https://openalex.org/W6637443312","https://openalex.org/W6675354045","https://openalex.org/W6686914157","https://openalex.org/W6713727841","https://openalex.org/W6743329288","https://openalex.org/W6966542651"],"related_works":["https://openalex.org/W2509319417","https://openalex.org/W4385175436","https://openalex.org/W2258907224","https://openalex.org/W3151444263","https://openalex.org/W2350890146","https://openalex.org/W2011516532","https://openalex.org/W2381907982","https://openalex.org/W2366668592","https://openalex.org/W646583987","https://openalex.org/W2350816699"],"abstract_inverted_index":{"Sports":[0],"analytics":[1,9],"in":[2,7,10,14,17,78,118,146,157],"general,":[3],"and":[4,45,75,91,97,161,207],"football":[5,58,65,198],"(soccer":[6],"USA)":[8],"particular,":[11],"have":[12],"evolved":[13],"recent":[15],"years":[16],"an":[18],"amazing":[19],"way,":[20],"thanks":[21],"to":[22,53,153],"automated":[23],"or":[24,129],"semi-automated":[25],"sensing":[26],"technologies":[27],"that":[28,47,142,163,192],"provide":[29],"high-fidelity":[30],"data":[31,63,199],"streams":[32],"extracted":[33],"from":[34],"every":[35],"game.":[36],"In":[37],"this":[38],"paper":[39],"we":[40,67],"propose":[41],"a":[42,50,69,84,95,100,124,173,193],"data-driven":[43],"approach":[44],"show":[46,162,169],"there":[48],"is":[49],"large":[51],"potential":[52,202],"boost":[54],"the":[55,79,88,92,103,119,133,143,147,154,158,177,185,188,201],"understanding":[56],"of":[57,64,71,94,115,172,187,203,209],"team":[59],"performance.":[60],"From":[61],"observational":[62],"games":[66],"extract":[68],"set":[70],"pass-based":[72],"performance":[73,134],"indicators":[74,135],"summarize":[76],"them":[77],"H":[80],"indicator.":[81],"We":[82,140],"observe":[83],"strong":[85],"correlation":[86],"among":[87],"proposed":[89,189],"indicator":[90],"success":[93],"team,":[96],"therefore":[98],"perform":[99],"simulation":[101],"on":[102,132,197],"four":[104],"major":[105],"European":[106],"championships":[107,149],"(78":[108],"teams,":[109],"almost":[110],"1500":[111],"games).":[112],"The":[113],"outcome":[114,126],"each":[116,138],"game":[117],"championship":[120],"was":[121],"replaced":[122],"by":[123],"synthetic":[125],"(win,":[127],"loss":[128],"draw)":[130],"based":[131],"computed":[136],"for":[137],"team.":[139],"found":[141],"final":[144],"rankings":[145,156],"simulated":[148],"are":[150,182],"very":[151],"close":[152],"actual":[155],"real":[159],"championships,":[160],"teams":[164],"with":[165],"high":[166],"ranking":[167],"error":[168],"extreme":[170],"values":[171],"defense/attack":[174],"efficiency":[175],"measure,":[176],"Pezzali":[178],"score.":[179],"Our":[180],"results":[181],"surprising":[183],"given":[184],"simplicity":[186],"indicators,":[190],"suggesting":[191],"complex":[194],"systems'":[195],"view":[196],"has":[200],"revealing":[204],"hidden":[205],"patterns":[206],"behavior":[208],"superior":[210],"quality.":[211]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":19},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":11},{"year":2016,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}