The　Video　game-Crowdsourcing　model　to　recollect　data　motivates　people　to　participate　by　entertaining　them.　Research　showed　that　the　solutions　players　make　in　this　model　are　competitive　against　experts　in　the　area.　Yet,　the　studies　in　the　area　focus　on　mimicking　people＇s　behavior,　including　their　mistakes.　Therefore,　we　use　a　Video　game-Crowdsourcing　to　model　a　problem　of　interest　to　find　strategies　for　it.　To　describe　matches　from　the　video　game　we　created,　we　designed　a　representation　that　simplifies　the　discovery　of　strategies.　Our　experimentation　compares　high　score　matches　against　low　score　ones　to　find　the　best　behaviors.　We　played　13　matches　employing　a　known　strategy　for　the　problem　to　validate　the　methodology.　Then,　we　applied　the　methodology　to　matches　from　players.　The　results　suggest　that　extracting　sub-sequences　is　a　process　to　find　strategies　and　that　we　can　use　them　to　design　algorithms　to　improve　current　algorithmic　solutions　for　that　problem.