Import driver ranking script for scouting
This is what we used at Madtown 2022 for scoring driver ability.
Will add test data in a follow up patch.
Change-Id: I5b5763f89ea1f6a4876be5af20553756f303b918
Signed-off-by: Michael Bick <mafbick@gmail.com>
diff --git a/scouting/DriverRank/Manifest.toml b/scouting/DriverRank/Manifest.toml
new file mode 100644
index 0000000..9422299
--- /dev/null
+++ b/scouting/DriverRank/Manifest.toml
@@ -0,0 +1,1652 @@
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+uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
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+
+[[deps.libblastrampoline_jll]]
+deps = ["Artifacts", "Libdl", "OpenBLAS_jll"]
+uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
+version = "5.1.1+0"
+
+[[deps.libfdk_aac_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
+git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55"
+uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
+version = "2.0.2+0"
+
+[[deps.libpng_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
+git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c"
+uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
+version = "1.6.38+0"
+
+[[deps.libvorbis_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
+git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c"
+uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
+version = "1.3.7+1"
+
+[[deps.nghttp2_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
+version = "1.48.0+0"
+
+[[deps.p7zip_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
+version = "17.4.0+0"
+
+[[deps.x264_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
+git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
+uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
+version = "2021.5.5+0"
+
+[[deps.x265_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
+git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
+uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
+version = "3.5.0+0"
+
+[[deps.xkbcommon_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
+git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a"
+uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
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diff --git a/scouting/DriverRank/Project.toml b/scouting/DriverRank/Project.toml
new file mode 100644
index 0000000..ccfaadb
--- /dev/null
+++ b/scouting/DriverRank/Project.toml
@@ -0,0 +1,21 @@
+name = "DriverRank"
+uuid = "591f49ca-476f-402a-ab3c-052881ce8c1d"
+authors = ["MBick1 "]
+version = "0.1.0"
+
+[deps]
+BlackBoxOptim = "a134a8b2-14d6-55f6-9291-3336d3ab0209"
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+GoogleSheets = "831f653e-6dbc-49a2-ac93-eebfaa09c6e6"
+HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5"
+Optim = "429524aa-4258-5aef-a3af-852621145aeb"
+PlotlyBase = "a03496cd-edff-5a9b-9e67-9cda94a718b5"
+PlotlyJS = "f0f68f2c-4968-5e81-91da-67840de0976a"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
+Roots = "f2b01f46-fcfa-551c-844a-d8ac1e96c665"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+Transducers = "28d57a85-8fef-5791-bfe6-a80928e7c999"
diff --git a/scouting/DriverRank/src/DriverRank.jl b/scouting/DriverRank/src/DriverRank.jl
new file mode 100644
index 0000000..c6e9aaa
--- /dev/null
+++ b/scouting/DriverRank/src/DriverRank.jl
@@ -0,0 +1,207 @@
+module DriverRank
+
+using GoogleSheets: sheets_client, Spreadsheet, CellRange, get, AUTH_SCOPE_READONLY
+using CSV
+using DataFrames: DataFrame
+using Transducers: Cat, MapCat, Map
+using DataStructures: OrderedSet
+using HypothesisTests: OneSampleZTest, pvalue
+using Roots: find_zero
+using Statistics: mean
+import Optim
+using Optim: optimize
+using BlackBoxOptim: bboptimize, best_candidate, best_fitness
+# using PlotlyJS
+using Plots: scatter, hline!, plotlyjs, savefig, plotly
+import PlotlyBase: to_html
+
+struct TeamKey
+ key::String
+end
+
+Base.@kwdef struct DriverMatchup{K}
+ winner::K
+ loser::K
+end
+
+Base.@kwdef struct DriverRankings{K}
+ team_keys::OrderedSet{K}
+ matchup_contributions::Matrix{Float64}
+ expected_win_rate_func
+end
+
+function DriverRankings(matchups::Vector{DriverMatchup{K}}) where K
+ team_keys =
+ matchups |>
+ MapCat(matchup -> (matchup.winner, matchup.loser)) |>
+ OrderedSet{K}
+ team_key_indecies =
+ zip(team_keys, 1:length(team_keys)) |>
+ Dict{TeamKey, Int}
+
+ matchup_contributions = zeros(length(matchups), length(team_keys))
+ for (i, matchup) in enumerate(matchups)
+ contribution = view(matchup_contributions, i, :)
+
+ winner_index = team_key_indecies[matchup.winner]
+ loser_index = team_key_indecies[matchup.loser]
+
+ contribution[winner_index] = 1
+ contribution[loser_index] = -1
+ end
+
+ # Create a distribution that represents
+ # how to translate player ranking point differences
+ # into win rates
+ point_difference = 100
+ win_rate_at_point_difference = 0.9
+ dist_std_dev = find_zero(
+ x -> win_rate_at_point_difference - pvalue(OneSampleZTest(point_difference, x, 1), tail=:left),
+ (0,Inf),
+ )
+ expected_win_rate_func(x) = pvalue(OneSampleZTest(x, dist_std_dev, 1), tail=:left)
+
+ return DriverRankings{K}(;
+ team_keys,
+ matchup_contributions,
+ expected_win_rate_func,
+ )
+end
+
+num_teams(dr::DriverRankings) = length(dr.team_keys)
+
+function objective_value(
+ driver_rankings::DriverRankings,
+ ranking_points::Vector{F},
+) where F
+ ranking_points_row = reshape(ranking_points, (1, num_teams(driver_rankings)))
+ return objective_value(driver_rankings, ranking_points_row)
+end
+
+function objective_value(
+ driver_rankings::DriverRankings,
+ ranking_points::Matrix{F},
+) where F
+ average_ranking_point_value::F =
+ mean(ranking_points)
+
+ k = 100 / length(ranking_points) # magic number
+ return -(k * log_likelihood(driver_rankings, ranking_points)) +
+ (average_ranking_point_value^2)
+end
+
+function log_likelihood(
+ driver_rankings::DriverRankings,
+ ranking_points::Matrix{F},
+) where F
+ matchup_ranking_point_differentials =
+ driver_rankings.matchup_contributions .* ranking_points |>
+ x -> sum(x, dims=2)
+
+ result::F =
+ driver_rankings.expected_win_rate_func.(matchup_ranking_point_differentials) |>
+ Map(log) |>
+ sum
+ return result
+end
+
+function rank()
+ # client = sheets_client(AUTH_SCOPE_READONLY)
+ # # spreadsheet_id = "13Cit7WrUxWz79iYVnoMoPc56W7H_cfr92jyT67tb2Xo"
+ # spreadsheet_id = "1q-Cl2aW4IkHk8Vcfd7OuFt0g4o3itn4SXgBi8Z1b7UE"
+ # range_name = "Form Responses 1"
+
+ # sheet = Spreadsheet(spreadsheet_id)
+ # range = CellRange(sheet, range_name)
+ # result = get(client, range).values
+
+ # # Filter empty rows
+ # is_not_empty = result[:, 1] .!= ""
+ # result = result[is_not_empty, :]
+ # df = DataFrame(TeamKey.(result[2:end, :]), result[1, :])
+
+ df = DataFrame(CSV.File("./data/2022_madtown.csv"))
+
+ # rank1 = "Rank 1 (best)"
+ # rank2 = "Rank 2"
+ # rank3 = "Rank 3"
+ # rank4 = "Rank 4"
+ # rank5 = "Rank 5"
+ # rank6 = "Rank 6 (worst)"
+ # matchups =
+ # [
+ # (df[!, rank1], df[!, rank2]),
+ # (df[!, rank1], df[!, rank3]),
+ # (df[!, rank1], df[!, rank4]),
+ # (df[!, rank1], df[!, rank5]),
+ # (df[!, rank1], df[!, rank6]),
+ # (df[!, rank2], df[!, rank3]),
+ # (df[!, rank2], df[!, rank4]),
+ # (df[!, rank2], df[!, rank5]),
+ # (df[!, rank2], df[!, rank6]),
+ # (df[!, rank3], df[!, rank4]),
+ # (df[!, rank3], df[!, rank5]),
+ # (df[!, rank3], df[!, rank6]),
+ # (df[!, rank4], df[!, rank5]),
+ # (df[!, rank4], df[!, rank6]),
+ # (df[!, rank5], df[!, rank6]),
+ # ] |>
+ # MapCat(((winners, losers),) -> zip(winners, losers)) |>
+ # Map(((winner, loser),) -> DriverMatchup(; winner, loser)) |>
+ # collect
+
+ rank1 = "Rank 1 (best)"
+ rank2 = "Rank 2"
+ rank3 = "Rank 3 (worst)"
+ rank_cols = [rank1, rank2, rank3]
+
+
+ df[!, rank_cols] = TeamKey.(df[!, rank_cols])
+ matchups =
+ [
+ (df[!, rank1], df[!, rank2]),
+ (df[!, rank1], df[!, rank3]),
+ (df[!, rank2], df[!, rank3]),
+ ] |>
+ MapCat(((winners, losers),) -> zip(winners, losers)) |>
+ Map(((winner, loser),) -> DriverMatchup(; winner, loser)) |>
+ collect
+
+ driver_rankings = DriverRankings(matchups)
+
+ # Optimize!
+ x0 = zeros(num_teams(driver_rankings))
+ res = optimize(x -> objective_value(driver_rankings, x), x0, Optim.LBFGS(), autodiff=:forward)
+
+ ranking_points =
+ DataFrame(
+ :team=>driver_rankings.team_keys |> Map(x -> x.key) |> collect,
+ :score=>Optim.minimizer(res),
+ ) |>
+ x -> sort!(x, [:score], rev=true)
+ show(ranking_points, allrows=true)
+
+ plotly()
+ idx = 1:length(ranking_points.team)
+ plt = scatter(
+ idx, ranking_points.score,
+ title="Driver Ranking",
+ xlabel="Team Number",
+ xticks=(idx, ranking_points.team),
+ xrotation=90,
+ ylabel="Score",
+ legend=false,
+ )
+ hline!(plt, [0.])
+
+ savefig(plt, "./driver_ranking.html")
+ # open("./driver_ranking.html", "w") do io
+ # PlotlyBase.to_html(io, plt)
+ # end
+
+ return plt
+end
+
+export rank
+
+end # module