diff --git a/docs/src/demos/.gitignore b/docs/src/demos/.gitignore index 7fd9a44..f332f54 100644 --- a/docs/src/demos/.gitignore +++ b/docs/src/demos/.gitignore @@ -1,2 +1,4 @@ *.md -!main.md \ No newline at end of file +!main.md +monkey-bmi-cache/data.mat +monkey-bmi-cache/ \ No newline at end of file diff --git a/docs/src/demos/04NBADemo.jl b/docs/src/demos/04NBADemo.jl new file mode 100644 index 0000000..650574f --- /dev/null +++ b/docs/src/demos/04NBADemo.jl @@ -0,0 +1,2627 @@ +### A Pluto.jl notebook ### +# v0.19.42 + +using Markdown +using InteractiveUtils + +# ╔═╡ 2ab7e40d-57cd-4f3d-90ad-ec32c69b37fb +using CSV, TensorCore, GCPDecompositions, LinearAlgebra, CairoMakie, DataFrames, Statistics, Polynomials, CacheVariables + +# ╔═╡ 6c5957c0-baec-42af-94ac-24064e1f6581 +using Downloads, ColorSchemes, DataFramesMeta, Cascadia,HTTP,Gumbo + +# ╔═╡ 8e067b40-3e2b-11ef-1ef3-a574b6ca03c4 +md""" +# NBA Full History +scraped data from the landofbasketball + + +**only records between active teams** + +""" + +# ╔═╡ 7cdd2f4a-a3e9-4a9b-a009-b31ab0e856de +html""" +
+
+ Monkey BMI Graphic + + +
+ Source: Paolo Uggetti, + The Ringer, 2021. +
+
+
+""" + +# ╔═╡ 02f06782-a038-4d4f-bd37-f4a31212907a +md""" +## Scrape and Create DataFrame +""" + +# ╔═╡ 42e0beef-000b-41a1-b6cb-6456a5da5eb5 +active_team_names = ["Atlanta Hawks","Boston Celtics", + "Cleveland Cavaliers", + "New Orleans Pelicans", + "Chicago Bulls", + "Dallas Mavericks", + "Denver Nuggets", + "Golden State Warriors", + "Houston Rockets", + "Los Angeles Clippers", + "Los Angeles Lakers", + "Miami Heat", + "Milwaukee Bucks", + "Minnesota Timberwolves", + "Brooklyn Nets", + "New York Knicks", + "Orlando Magic", + "Indiana Pacers", + "Philadelphia 76ers", + "Phoenix Suns", + "Portland Trail Blazers", + "Sacramento Kings", + "San Antonio Spurs", + "Oklahoma City Thunder", + "Toronto Raptors", + "Utah Jazz", + "Memphis Grizzlies", + "Washington Wizards", + "Detroit Pistons", + "Charlotte Hornets"] + + +# ╔═╡ 74cb492d-7b9a-49e8-a8e3-16ac2e3448ca +num_teams = length(active_team_names) + +# ╔═╡ eaa32830-0a48-4124-8088-f5bc7a9b4ff3 +num_years = length(1946:2023) + +# ╔═╡ 60fda4f4-4c0a-4c24-bf6c-a6944d00c5a7 +year_range = 1946:2023 + +# ╔═╡ 4bcc5961-f328-40aa-a0dc-f1755fa58e72 +active_team_ids = [1610612737, 1610612738, 1610612739, 1610612740, 1610612741, 1610612742, 1610612743, 1610612744, 1610612745, 1610612746, 1610612747, 1610612748, 1610612749, 1610612750, 1610612751, 1610612752, 1610612753, 1610612754, 1610612755, 1610612756, 1610612757, 1610612758, 1610612759, 1610612760, 1610612761, 1610612762, 1610612763, 1610612764, 1610612765, 1610612766] + +# ╔═╡ 2e4faef9-d8a2-4a8f-8e83-a9c5b280d174 +id_team_dict = Dict(zip(active_team_ids, active_team_names)) + +# ╔═╡ 7a20002f-5724-46dc-b147-af0d42c8523c +team_id_dict = Dict(zip(active_team_names, active_team_ids)) + +# ╔═╡ d20c9cd7-6726-4431-98d7-4ca07bc70b5a +old_names = Dict( + "Buffalo Braves" => "Los Angeles Clippers", + "New Orleans Jazz" => "Utah Jazz", + "Seattle Supersonics" => "Oklahoma City Thunder", + "Washington Bullets" => "Washington Wizards", + "Baltimore Bullets" => "Washington Wizards", + "Cincinnati Royals" => "Sacramento Kings", + "San Francisco Warriors" => "Golden State Warriors", + "Philadelphia Warriors" => "Golden State Warriors", + "St. Louis Hawks" => "Atlanta Hawks", + "San Diego Rockets" => "Houston Rockets", + "Minneapolis Lakers" => "Los Angeles Lakers", + "Syracuse Nationals" => "Philadelphia 76ers", + "New Jersey Americans" => "Brooklyn Nets", + "Milwaukee Hawks" => "Atlanta Hawks", + "Chicago Zephyrs" => "Washington Wizards", + "Tri-Cities Blackhawks" => "Atlanta Hawks", + "Fort Wayne Pistons" => "Detroit Pistons", + "Kansas City-Omaha Kings" => "Sacramento Kings", + "New York Nets" => "Brooklyn Nets", + "New Jersey Nets" => "Brooklyn Nets", + "San Diego Clippers" => "Los Angeles Clippers", + "Kansas City Kings" => "Sacramento Kings", + "Vancouver Grizzlies" => "Memphis Grizzlies", + "New Orleans Hornets" => "New Orleans Pelicans", + "New Orleans/Oklahoma City Hornets" => "New Orleans Pelicans", + "Charlotte Bobcats" => "Charlotte Hornets", + "Chicago Packers" => "Washington Wizards", + "Capital Bullets" => "Washington Wizards", + "Rochester Royals" => "Sacramento Kings") + +# ╔═╡ 2d3441eb-3cf9-49c0-b09e-52f5e235c8ca +all_games = with_theme() do + function scrape_season_data(start_year::Int, end_year::Int, active_team_names::Vector{String}, team_id_dict::Dict{String, Int}, old_names::Dict{String, String}) + + # Create an empty DataFrame for looping + full_df = DataFrame( + season_id = Int[], + team_id_home = Int[], + team_name_home = String[], + wl_home = String[], + team_id_away = Int[], + team_name_away = String[], + plus_minus_home = Int[]) + + # Loop through each season + for year in start_year:end_year + url = "https://www.landofbasketball.com/results/$(year)_$(year+1)_scores_full.htm" + + # Get content + response = HTTP.get(url) + html_content = String(response.body) + + parsed_html = parsehtml(html_content) + + # Find tables with scores + table_selector = Selector("table") + tables = eachmatch(table_selector, parsed_html.root) + + # Create empty arrays to push to + dates = String[] + home_teams = String[] + home_scores = String[] + away_teams = String[] + away_scores = String[] + + for table in tables + # Loop through each row in the table + row_selector = Selector("tr") + rows = eachmatch(row_selector, table)[2:end] + + # Skip the titles + for row in rows + column_selector = Selector("td div.left") + columns = eachmatch(column_selector, row) + + # Choose the columns + if length(columns) >= 4 + home_team = strip(Gumbo.text(columns[2])) + home_score = strip(Gumbo.text(columns[3])) + away_team = strip(Gumbo.text(columns[6])) + away_score = strip(Gumbo.text(columns[7])) + score_match = match(r"^\d+", away_score) + if score_match !== nothing + away_score = score_match.match + else + away_score = "" + end + # Append stats + push!(home_teams, home_team) + push!(home_scores, home_score) + push!(away_teams, away_team) + push!(away_scores, away_score) + end + end + end + + # Create a DataFrame for the current season + df = DataFrame( + season_id = fill(year, length(home_teams)), + Home_Team = home_teams, + Home_Score = home_scores, + Away_Team = away_teams, + Away_Score = away_scores + ) + + # Convert scores to integers + df[!, :Home_Score] .= [parse(Int, x) for x in df[!, :Home_Score]] + df[!, :Away_Score] .= [parse(Int, x) for x in df[!, :Away_Score]] + + # Rename teams to updated/current name + function rename_teams!(df::DataFrame, mapping::Dict{String, String}) + for row in eachrow(df) + if haskey(mapping, row.Home_Team) + row.Home_Team = mapping[row.Home_Team] + end + if haskey(mapping, row.Away_Team) + row.Away_Team = mapping[row.Away_Team] + end + end + end + + rename_teams!(df, old_names) + + # Filter the DataFrame to include only active teams + filtered_df = filter(row -> row.Home_Team in active_team_names && row.Away_Team in active_team_names, df) + + # Create stat columns from website data + filtered_df[!, :team_id_home] = [team_id_dict[row.Home_Team] for row in eachrow(filtered_df)] + filtered_df[!, :team_id_away] = [team_id_dict[row.Away_Team] for row in eachrow(filtered_df)] + + + filtered_df[!, :wl_home] = [row.Home_Score > row.Away_Score ? "W" : "L" for row in eachrow(filtered_df)] + + + filtered_df[!, :plus_minus_home] = [row.Home_Score - row.Away_Score for row in eachrow(filtered_df)] + filtered_df[!, :season_id] .= year + + rename!(filtered_df, :Home_Team => :team_name_home, :Away_Team => :team_name_away) + + # Select columns in order and put into big DataFrame + append!(full_df, select(filtered_df, [:season_id, :team_id_home, :team_name_home, :wl_home, :team_id_away, :team_name_away, :plus_minus_home])) + end + + return full_df +end + + # Scraping function + full_season_46_to_23 = scrape_season_data(1946, 2023, active_team_names, team_id_dict, old_names) +end + +# ╔═╡ 1eaa449c-4a13-4225-8d5a-7c9a2cdf2949 +md""" +There are 68,761 games in all of NBA history between the current 30 teams. Not including playoffs, (to showcase some predictive power) there have been 64,400 regular season games. +""" + +# ╔═╡ 9c87cbe5-9f4a-476f-870f-8f6c4fe8e58c +reg_season = with_theme() do + function scrape_season_data(start_year::Int, end_year::Int, active_team_names::Vector{String}, team_id_dict::Dict{String, Int}, old_names::Dict{String, String}) + + # Create an empty DataFrame for looping + full_df = DataFrame( + season_id = Int[], + team_id_home = Int[], + team_name_home = String[], + wl_home = String[], + team_id_away = Int[], + team_name_away = String[], + plus_minus_home = Int[] + ) + + # Loop through each season + for year in start_year:end_year + url = "https://www.landofbasketball.com/results/$(year)_$(year+1)_scores_full.htm" + + # Get content + response = HTTP.get(url) + html_content = String(response.body) + parsed_html = parsehtml(html_content) + + # Create empty arrays to push to + home_teams = String[] + home_scores = String[] + away_teams = String[] + away_scores = String[] + + # Track whether we are processing playoff tables + processing_playoffs = false + + # Find tables only before playoffs + function process_node(node) + if typeof(node) <: Gumbo.HTMLElement + tagname = Gumbo.tag(node) + if tagname == :h3 && strip(Gumbo.text(node)) == "Full List - NBA Playoffs" + processing_playoffs = true + elseif tagname == :table && !processing_playoffs + process_table(node) + end + end + + for child in Gumbo.children(node) + process_node(child) + end + end + + function process_table(table) + + # Loop through each row in the table + rows = eachmatch(Selector("tr"), table)[2:end] + + for row in rows + columns = eachmatch(Selector("td div.left"), row) + + # Choose the columns + if length(columns) >= 4 + home_team = strip(Gumbo.text(columns[2])) + home_score = strip(Gumbo.text(columns[3])) + away_team = strip(Gumbo.text(columns[6])) + away_score = strip(Gumbo.text(columns[7])) + score_match = match(r"^\d+", away_score) + + if score_match !== nothing + away_score = score_match.match + else + away_score = "" + end + + # Append stats + push!(home_teams, home_team) + push!(home_scores, home_score) + push!(away_teams, away_team) + push!(away_scores, away_score) + end + end + end + + process_node(parsed_html.root) + + # Create a DataFrame for the current season + df = DataFrame( + season_id = fill(year, length(home_teams)), + Home_Team = home_teams, + Home_Score = home_scores, + Away_Team = away_teams, + Away_Score = away_scores + ) + + # Convert scores to integers + df[!, :Home_Score] .= [parse(Int, x) for x in df[!, :Home_Score]] + df[!, :Away_Score] .= [parse(Int, x) for x in df[!, :Away_Score]] + + # Rename teams to updated/current name + function rename_teams!(df::DataFrame, mapping::Dict{String, String}) + for row in eachrow(df) + if haskey(mapping, row.Home_Team) + row.Home_Team = mapping[row.Home_Team] + end + if haskey(mapping, row.Away_Team) + row.Away_Team = mapping[row.Away_Team] + end + end + end + + rename_teams!(df, old_names) + + # Only include active teams + filtered_df = filter(row -> row.Home_Team in active_team_names && row.Away_Team in active_team_names, df) + + # Create stat columns from website data + filtered_df[!, :team_id_home] = [team_id_dict[row.Home_Team] for row in eachrow(filtered_df)] + filtered_df[!, :team_id_away] = [team_id_dict[row.Away_Team] for row in eachrow(filtered_df)] + filtered_df[!, :wl_home] = [row.Home_Score > row.Away_Score ? "W" : "L" for row in eachrow(filtered_df)] + filtered_df[!, :plus_minus_home] = [row.Home_Score - row.Away_Score for row in eachrow(filtered_df)] + filtered_df[!, :season_id] .= year + + rename!(filtered_df, :Home_Team => :team_name_home, :Away_Team => :team_name_away) + + # Select columns and put into big DataFrame + append!(full_df, select(filtered_df, [:season_id, :team_id_home, :team_name_home, :wl_home, :team_id_away, :team_name_away, :plus_minus_home])) + end + + return full_df + end + + # Scraping function + full_season_46_to_23 = scrape_season_data(1946, 2023, active_team_names, team_id_dict, old_names) +end + +# ╔═╡ 0b298b6a-f736-4c66-a8d0-bb1b3bda0f6a +colors = [:orangered, :green3, :red4, :navy, :firebrick2,:steelblue2,:gold,:deepskyblue3,:firebrick3,:royalblue1,:mediumorchid4,:firebrick4,:darkgreen,:chartreuse3,:black,:chocolate1,:deepskyblue2,:yellow2,:crimson,:darkorange1,:red3,:rebeccapurple,:gray69,:salmon,:firebrick,:green,:cornflowerblue,:navyblue,:red,:darkcyan] + +# ╔═╡ be70f54d-ce70-4886-a4e2-5d5734d79f61 +scheme = ColorSchemes.distinguishable_colors(30) + +# ╔═╡ 37889ed2-104f-4441-b85a-47c2d03adf0e +md""" +# Regular Season Games +""" + +# ╔═╡ 52358d59-c78d-465a-8d2b-bbc6f3e31a78 +begin +X = zeros(Int, num_teams, num_teams, num_years); +for row in eachrow(reg_season) + i_home = findfirst(x -> x == row.team_id_home, active_team_ids) + j_away = findfirst(x -> x == row.team_id_away, active_team_ids) + k = findfirst(x -> x == row.season_id, year_range) + + if row.wl_home == "W" + # Home team won + X[i_home, j_away, k] += 1 + else + # Away team won + X[j_away, i_home, k] += 1 + end +end + + X = Int.(X) +end; + +# ╔═╡ c867179d-efdb-4bb5-84f6-92fb6b400232 +md""" +## Data Visualization +""" + +# ╔═╡ 9c959be1-8bbb-4dc3-9da7-370927d18e38 +@cache "records-bargraph.bson" with_theme() do + fig = Figure(size = (1000,3000)) + axes = [] + + n_cols = 2 + + # Set super title + fig[0, 1:n_cols] = Label(fig,"NBA Team Performance Over the Years", fontsize = 25, halign = :center,tellwidth = false, tellheight = true, font = "Bold Arial") + + + # Loop through each team + for idx in 1:length(active_team_names) + + # Calculate the team's record for each year + team_performance = sum(X[idx, :, :], dims = 1)[:] + + # Determine subplot position + row = div(idx - 1, n_cols) + 1 + col = (idx - 1) % n_cols + 1 + + # Create a bar plot for the team + ax = Axis(fig[row, col], xticks = 1946:20:2026, + title = active_team_names[idx], xlabel = "Start of Season Year", ylabel = "Record") + push!(axes,ax) + barplot!(ax, 1946:2023, team_performance; color = colors[idx]) + + # Make middle graphs unlabeled + if fldmod1(idx, n_cols)[2] != 1 && fldmod1(idx, n_cols)[2] != n_cols + ax.ylabelvisible = false + end + + if fldmod1(idx, n_cols)[1] != 15 + ax.xlabelvisible = false + ax.xticklabelsvisible = false + end + + # Flip and switch y-axis of last column + if fldmod1(idx, n_cols)[2] == n_cols + ax.yaxisposition = :right + ax.ylabelrotation = 3pi/2 + end + + # Link y axes + + linkyaxes!(axes...) + end + + HTML(repr("text/html", fig)) +end + +# ╔═╡ d9507fad-dedd-44a2-b60f-cf91bebcc2ba +md""" +These bar graphs show when teams were inaugurated, re-franchised (Charlotte), and their records throughout the years. +""" + +# ╔═╡ 24a3e00b-bc95-42a6-99da-a7bdcb6fd23b +html""" +
+
+ Monkey BMI Graphic + + +
+ Source: +NBA License, + NBA, 2021. +
+
+
+ +""" + +# ╔═╡ 9ea3a825-7239-42de-ba57-09ddd49af582 +@cache "record-linegraph.bson" with_theme() do + + fig = Figure(size = (900, 800)) + + ax = Axis(fig[1, 1], title = "NBA Team Trends", xlabel = "Start of Season Year", ylabel = "Record", titlesize = 25, ylabelsize = 18, xlabelsize = 18, xticks = 1946:20:2026) + + # Loop through each team and plot their performance + for idx in 1:length(active_team_names) + + # Calculate record per year + team_performance = sum(X[idx, :, :], dims = 1)[:] + + # Filter out the years where the team's record is zero + valid_indices = findall(x -> x != 0, team_performance) + + if length(valid_indices) > 0 + filtered_years = year_range[valid_indices] + filtered_performance = team_performance[valid_indices] + + # Fit linearly + p = fit(filtered_years, filtered_performance, 1) + + # Get points on line + poly_years = range(minimum(filtered_years), stop=maximum(filtered_years), length=100) + poly_vals = p.(poly_years) + + # Plot trendline + lines!(ax, poly_years, poly_vals, label = active_team_names[idx], color = colors[idx], linewidth=2) + + # Add a dot at the start and arrows at the end + scatter!(ax, [filtered_years[1]], [p(filtered_years[1])], color = colors[idx], markersize=10, marker=:circle) + + dx = poly_years[end] - poly_years[end-1] + dy = poly_vals[end] - poly_vals[end-1] + + arrows!(ax, [poly_years[end-1]], [poly_vals[end-1]], [dx], [dy], color=colors[idx]) + end + end + + # Add a legend + Legend(fig[1, 2], ax, "Teams", position = :rb) + + HTML(repr("text/html", fig)) +end + +# ╔═╡ f38f5ea4-ec79-4cac-b391-df1eb54fd551 +md""" +This line graph shows the overall trend of each team's performance (fit to a first degree polynomial). +""" + +# ╔═╡ 1eeea4cd-e91d-4689-9f04-7dd988ced77c +@cache "record-heatmap.bson" with_theme() do + teams = 1:length(active_team_names) + performance_data = [sum(X[team, :, :], dims = 1)[:] for team in teams] + + # Combine the performance data into a matrix + performance_matrix = hcat(performance_data...) + + # Create the heatmap + fig = Figure(size = (1200, 1000)) + + ax = Axis(fig[1, 1], + title = "NBA Team History", + xlabel = "Start of Season Year", + ylabel = "Teams", + xticks = (1:10:length(year_range), string.(year_range[1:10:end])), + yticks = (1:length(active_team_names), active_team_names), + xticklabelrotation = pi/4; titlesize = 30, ylabelsize = 24, xlabelsize = 24, + xticklabelsize = 20, yticklabelsize = 15 + ) + + heatmap!(ax, performance_matrix; colormap = (:plasma)) + + Colorbar(fig[:, end+1], label = "Performance", vertical = true,colormap = :plasma, labelsize = 20) + + HTML(repr("text/html", fig)) +end + +# ╔═╡ cb4387d8-1bee-464f-8fc1-1322a276c473 +md""" +This heatmap is a visually easier graph for determining the prime eras for each team and their inaugural years. +""" + +# ╔═╡ 1aca785f-c8a7-4619-8985-557ad275092b +md""" +## Third Order Tensor Decomposition +""" + +# ╔═╡ c1fc702b-0fb0-48c5-af4d-79bfeb95c908 +M = gcp(X,12;loss = GCPLosses.NonnegativeLeastSquares()) + +# ╔═╡ 729caa2a-0a5d-471d-a75f-6b4a51c50808 +@cache "thirdorder-decomp.bson" with_theme() do + fig = Figure(size = (2000, 4100)) + + # Set up axes and plot each factor matrix + for row in 1:ncomponents(M) + ax1 = Axis(fig[row+1, 1]) + data1 = LinearAlgebra.normalize(M.U[1][:, row], Inf) + sorted_indices1 = sortperm(data1, rev=true) + bar_colors1 = fill(:dodgerblue, length(data1)) + + # Change color for the three tallest bars + bar_colors1[sorted_indices1[1:2]] .= :navy + + # Plot and adjust tick labels + barplot!(ax1, 1:size(X, 1), data1; color = bar_colors1) + ax1.xticks = (1:length(active_team_names), active_team_names) + ax1.xticklabelrotation = pi/4 + + ax2 = Axis(fig[row+1, 2]) + data2 = LinearAlgebra.normalize(M.U[2][:, row], Inf) + sorted_indices2 = sortperm(data2, rev=true) + bar_colors2 = fill(:indianred1, length(data2)) + + # Change color for the three tallest bars + bar_colors2[sorted_indices2[1:2]] .= :darkred + + # Plot and adjust tick labels + barplot!(ax2, 1:size(X, 2), data2; color = bar_colors2) + ax2.xticks = (1:length(active_team_names), active_team_names) + ax2.xticklabelrotation = pi/4 + + # Plot third matrix + ax3 = Axis(fig[row+1, 3], xticks = (0:20:80, ["1946", "1966", "1986", "2006", "2026"])) + lines!(ax3, 1:size(X, 3), LinearAlgebra.normalize(M.U[3][:, row], Inf)) + end + + # Link and hide axes + for axis in 1:3 + linkxaxes!(contents(fig[:, axis])...) + linkyaxes!(contents(fig[:, axis])...) + end + + # Add labels and super title + labels = ["Power Teams", "Weak Teams", "Year"] + for i in 1:3 + Label(fig[1, i], labels[i]; tellwidth = false, fontsize = 35, font = "Bold Arial") + end + + fig[0, 1:3] = Label(fig, "NBA Regular Season Tensor Decomposition", fontsize = 60, halign = :center, valign = :bottom, tellwidth = false, font = "Bold Arial") + HTML(repr("text/html", fig)) + +end + +# ╔═╡ 9627bf54-da57-4c89-afa4-f994341cf27c +md""" +This tensor decomposition accurately predicts the NBA champions throughout the years. It captures the Bull's dominance in the 90s, Heat short after, Celtics in the 60s and currently, Lakers in the 2000s, 80s, and 50s, Warriors in the late 2010s, and the upcoming Nuggets, Mavericks, etc. +""" + +# ╔═╡ be21b5a6-17db-4675-86e1-25051f9ad26b +md""" +# Fourth Order Tensor +""" + +# ╔═╡ 0b43b746-74ca-4df4-bd03-1c8335dc2def +begin + Y = zeros(Int, num_teams, num_teams, num_years, 2) + + for row in eachrow(reg_season) + i_home = findfirst(x -> x == row.team_id_home, active_team_ids) + j_away = findfirst(x -> x == row.team_id_away, active_team_ids) + k = findfirst(x -> x == row.season_id, year_range) + + if row.wl_home == "W" + # Home team won + Y[i_home, j_away, k, 1] += 1 + + # Point differential + Y[i_home, j_away, k, 2] += row.plus_minus_home + else + # Away team won + Y[j_away, i_home, k, 1] += 1 + + # Point differential + Y[j_away, i_home, k, 2] -= row.plus_minus_home + end + end + + + Y = Int.(Y) +end; + +# ╔═╡ 73eb1b18-55c7-4385-9993-b71675f11443 +md""" +## Data Visualization +""" + +# ╔═╡ 29b56156-58d6-4ea9-9075-40c2c5474636 +@cache "margin-heatmap.bson" with_theme() do + # Find total point differentials for each team per year + team_point_differentials = zeros(Int, num_teams, num_years) + + for i in 1:num_teams + for k in 1:num_years + team_point_differentials[i, k] = sum(Y[i, :, k, 2]) - sum(Y[:, i, k, 2]) + end + end + + # Create the heatmap + fig = Figure(size = (1200, 1000)) + + ax = Axis(fig[1, 1], + title = "NBA Team Point Differentials", + ylabel = "Start of Season Year", + xlabel = "Teams", + yticks = (1:10:length(year_range), string.(year_range[1:10:end])), + xticks = (1:length(active_team_names), active_team_names), + xticklabelrotation = pi/4; titlesize = 30, ylabelsize = 20, xlabelsize = 20 + ) + + heatmap!(ax, team_point_differentials; colormap = (:viridis)) + + Colorbar(fig[:, end+1], label = "Margin", vertical = true,colormap = :viridis, labelsize = 20) + + HTML(repr("text/html", fig)) +end + + + +# ╔═╡ 29d68c9b-1fb8-4c6f-b991-59175b0d89de +md""" +This heatmap shows the overall point differentials per team over the years (ex. bright yellow representing a huge winning margin). +""" + +# ╔═╡ 9a1d6cca-3ff3-4a56-b82d-3b0fd3daee0a +@cache "margin-linegraph.bson" with_theme() do + fig = Figure(size = (900, 800)) + + ax = Axis(fig[1, 1], title = "NBA Point Margin Trend", xlabel = "Start of Season Year", ylabel = "Point Differential", titlesize = 25, ylabelsize = 18, xlabelsize = 18, xticks = 1946:10:2026) + + # Loop through each team and plot their pd + for idx in 1:length(active_team_names) + + # Calculate the team's pd for each year + team_point_differentials = [sum(Y[idx, :, k, 2]) - sum(Y[:, idx, k, 2]) for k in 1:num_years] + + years = 1946:2023 + + # Filter out the years where the team's record is zero + valid_indices = findall(x -> x != 0, team_point_differentials) + if length(valid_indices) > 0 + filtered_years = years[valid_indices] + filtered_pd = team_point_differentials[valid_indices] + + # Fit a polynomial to the filtered pd data + p = fit(filtered_years, filtered_pd, 1) + + # Get datapoints for trendline + poly_years = range(minimum(filtered_years), stop=maximum(filtered_years), length=100) + poly_vals = p.(poly_years) + + # Plot the polynomial trendline + lines!(ax, poly_years, poly_vals, label = active_team_names[idx], color = colors[idx], linewidth=2) + + # Give lines a start and end marker + scatter!(ax, [filtered_years[1]], [p(filtered_years[1])], color = colors[idx], markersize=10, marker=:circle) + + dx = poly_years[end] - poly_years[end-1] + dy = poly_vals[end] - poly_vals[end-1] + + + arrows!(ax, [poly_years[end-1]], [poly_vals[end-1]], [dx], [dy], color=colors[idx]) + end + end + + # Add a legend + Legend(fig[1, 2], ax, "Teams", position = :rb) + + HTML(repr("text/html", fig)) +end + +# ╔═╡ 7c87de40-919f-445b-a767-d0ef1f14e44a +md""" +This line graph shows the trend of how badly teams either win or lose throughout the years (fit to a first degree polynomial). +""" + +# ╔═╡ 7d3b3c72-0ac3-4cb8-a430-04a1ea12ad0f +@cache "margin-bargraphs.bson" with_theme() do + fig = Figure(size = (1000,3000)) + axes = [] + + n_cols = 2 + + # Set super title + fig[0, 1:n_cols] = Label(fig,"NBA Point Margins Over the Years", fontsize = 25, halign = :center,tellwidth = false, tellheight = true, font = "Bold Arial") + + # Loop through each team + for idx in 1:length(active_team_names) + + # Calculate the total point differential per year + team_point_differentials = [sum(Y[idx, :, k, 2]) - sum(Y[:, idx, k, 2]) for k in 1:num_years] + + # Determine subplot position + row = div(idx - 1, n_cols) + 1 + col = (idx - 1) % n_cols + 1 + + # Create a bar plot for the team + ax = Axis(fig[row, col], xticks = 1946:20:2026, title = active_team_names[idx], xlabel = "Start of Season Year", ylabel = "Total Point Differential") + + push!(axes,ax) + barplot!(ax, 1946:2023, team_point_differentials; color = colors[idx]) + + # Make middle graphs unlabeled + if fldmod1(idx, n_cols)[2] != 1 && fldmod1(idx, n_cols)[2] != n_cols + ax.ylabelvisible = false + end + + if fldmod1(idx, n_cols)[1] != 15 + ax.xlabelvisible = false + ax.xticklabelsvisible = false + end + + # Flip and switch y-axis of last column + if fldmod1(idx, n_cols)[2] == n_cols + ax.ylabelvisible = false + ax.yticklabelsvisible = false + end + + # Link y axes + linkyaxes!(axes...) + end + + HTML(repr("text/html", fig)) +end + +# ╔═╡ 3265f499-c53d-49d2-8826-499b656013e7 +md""" +These bar graphs are another representation of the point margins throughout the years, but with an emphasis on each team individually (not for easy comparison). +""" + +# ╔═╡ a1fce48b-79f3-4f19-b488-2b6a5d5b25e6 +md""" +## Fourth Order Tensor Decomposition + +""" + +# ╔═╡ 1a8401dc-0950-40d8-99c8-4440fd43483f +# rank 11 works well +M_fourth = gcp(Y,11;loss = GCPLosses.NonnegativeLeastSquares()) + +# ╔═╡ e271d658-6147-4671-81cc-9e76c205d0b2 +@cache "fourthorder-decomp.bson" with_theme() do + fig = Figure(size = (2500, 3500)) + years = 1946:2023 + # Set up axes and plot + for row in 1:ncomponents(M_fourth) + ax1 = Axis(fig[row+1, 1]) + data1 = LinearAlgebra.normalize(M_fourth.U[1][:, row], Inf) + sorted_indices1 = sortperm(data1, rev=true) + bar_colors1 = fill(:dodgerblue, length(data1)) + bar_colors1[sorted_indices1[1:3]] .= :navy # Change color for the three tallest bars + barplot!(ax1, 1:size(Y, 1), data1; color = bar_colors1) + ax1.xticks = (1:length(active_team_names), active_team_names) + ax1.xticklabelrotation = pi/4 + + ax2 = Axis(fig[row+1, 2]) + data2 = LinearAlgebra.normalize(M_fourth.U[2][:, row], Inf) + sorted_indices2 = sortperm(data2, rev=true) + bar_colors2 = fill(:indianred1, length(data2)) + bar_colors2[sorted_indices2[1:3]] .= :darkred # Change color for the three tallest bars + barplot!(ax2, 1:size(Y, 2), data2; color = bar_colors2) + ax2.xticks = (1:length(active_team_names), active_team_names) + ax2.xticklabelrotation = pi/4 + + ax3 = Axis(fig[row+1, 3], xticks = (0:20:80, ["1946", "1966", "1986", "2006", "2026"])) + + lines!(ax3, 1:size(Y, 3), LinearAlgebra.normalize(M_fourth.U[3][:, row], Inf), color = :indianred1) + ax4 = Axis(fig[row+1, 4]) + total = sum(M_fourth.U[4][2,:]) + ax4.xticklabelsvisible = false + ax4.yticklabelsvisible = false + ax4.xticksvisible = false + ax4.yticksvisible = false + pd_percent = M_fourth.U[4][2, row] * 100 + pie!(ax4, [pd_percent, 100 - pd_percent], label=["$(round(pd_percent, digits=1))%", ""],color = [:dodgerblue, :lightgrey]) + end + + # Link and hide axes + for axis in 1:4 + linkxaxes!(contents(fig[:, axis])...) + linkyaxes!(contents(fig[:, axis])...) + end + + # Add labels and super title + labels = ["Power Teams", "Weak Teams", "Year","Point Differential"] + for i in 1:4 + Label(fig[1, i], labels[i]; tellwidth = false, fontsize = 35, font = "Bold Arial") + end + + fig[0, 1:4] = Label(fig, "Four Way NBA Tensor Decomposition", fontsize = 60, halign = :center, valign = :bottom, tellwidth = false, font = "Bold Arial") + + HTML(repr("text/html", fig)) +end + +# ╔═╡ a6f55950-d1bd-4a3c-89b3-712c02d19e1e +md""" +This tensor decomposition shows the dominance of power teams throughout the years, but it adds a fourth component of the point differentials. The pie charts show how much these teams contribute to huge point differentials (huge percentages for the Celtics and Lakers in the corresponding eras). +""" + +# ╔═╡ 4eca0dde-4557-4c7d-a49a-96a664bd3aba +md""" +## References +1. "Land of Basketball", LandOfBasketball.com, 2024. [https://www.landofbasketball.com/](https://www.landofbasketball.com/) + + +2. "NBA Official Site", NBA.com, 2024. 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