File size: 6,709 Bytes
0247f30
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
#QUEENS PICTH DESIGN APP API

library(plumber)
library(mgcv)
library(data.table)
library(jsonlite)

cat("Loading model artifacts...\n")
fit          <- readRDS("fit_l6_champ.rds")
vaa_mod      <- readRDS("vaa_anchor_model.rds")
haa_mod      <- readRDS("haa_anchor_model.rds")
scaling      <- readRDS("shape_scaling_factors.rds")
mu_l6        <- scaling$mu_l6
sig_l6       <- scaling$sig_l6
train_means  <- fread("train_means.csv")

model_levels <- c("FF","CU","ST","SI","FC","SL","CH","FS","KC","SV","KN","EP","FA","FO","CS","SC")

cat(sprintf("Model ready. %d pitch types loaded.\n", length(model_levels)))

cat("Loading pitcher profiles...\n")
pitcher_profiles <- tryCatch({
  dt <- fread("pitcher_profiles.csv")
  cat(sprintf("Pitcher profiles loaded: %d rows, %d pitchers.\n",
              nrow(dt), dt[, uniqueN(player_name)]))
  dt
}, error = function(e) {
  warning(sprintf("Failed to load pitcher_profiles.csv: %s", conditionMessage(e)))
  NULL
})

build_features <- function(d) {
  d <- as.data.table(d)

  d[, p_throws := fifelse(p_throws %in% c("Left","LHP"), "L",
                  fifelse(p_throws %in% c("Right","RHP"), "R", p_throws))]

  d[, pfx_x_sym   := fifelse(p_throws == "L", pfx_x * -1,   pfx_x)]
  d[, plate_x_sym := fifelse(p_throws == "L", plate_x * -1, plate_x)]
  d[, rel_x_sym   := fifelse(p_throws == "L", release_pos_x * -1, release_pos_x)]

  if (!"release_extension" %in% names(d)) d[, release_extension := 6.0]
  d[is.na(release_extension), release_extension := 6.0]

  d[, flight_time   := (1.4167 - 50) / vy0]
  d[, vaa           := atan((vz0 + az * flight_time) / -(vy0 + ay * flight_time)) * (180 / pi)]
  d[, haa           := atan((vx0 + ax * flight_time) / -(vy0 + ay * flight_time)) * (180 / pi)]
  d[, haa_sym       := fifelse(p_throws == "L", haa * -1, haa)]

  d[, vaa_resid     := vaa     - predict(vaa_mod, newdata = d)]
  d[, haa_resid_sym := haa_sym - predict(haa_mod, newdata = d)]

  d <- merge(d, train_means, by = "pitch_type", all.x = FALSE)
  d[, ivb_diff    := pfx_z     - base_ivb]
  d[, hb_diff_sym := pfx_x_sym - base_hb]

  d[, pitch_type := factor(as.character(pitch_type), levels = model_levels)]
  d[, p_throws   := factor(as.character(p_throws), levels = c("L", "R"))]

  d[!is.na(pitch_type)]
}

score_pitches <- function(d) {
  d <- build_features(d)
  if (nrow(d) == 0) return(list(error = "No valid pitches after feature engineering"))

  raw       <- predict(fit, newdata = d)
  shape_plus <- (((raw * -1) - mu_l6) / sig_l6) * 10 + 100

  term_mat  <- predict(fit, newdata = d, type = "terms")
  col_names <- colnames(term_mat)

  list(
    n          = nrow(d),
    shape_plus = round(as.numeric(shape_plus), 1),
    terms      = lapply(seq_len(nrow(d)), function(i) {
      row <- as.list(round(term_mat[i, ], 3))
      names(row) <- col_names
      row
    })
  )
}


#* @apiTitle Queens Shape+ API
#* @apiDescription Scores pitch shapes using the Shape+ v2.0 GAM model

#* @filter cors
function(req, res) {
  res$setHeader("Access-Control-Allow-Origin",  "*")
  res$setHeader("Access-Control-Allow-Methods", "GET, POST, OPTIONS")
  res$setHeader("Access-Control-Allow-Headers", "Content-Type")
  if (req$REQUEST_METHOD == "OPTIONS") {
    res$status <- 200
    return(list())
  }
  plumber::forward()
}

#* @post /score
#* @serializer json
function(req) {
  tryCatch({
    body <- fromJSON(req$postBody, simplifyDataFrame = TRUE)
    score_pitches(as.data.table(body))
  }, error = function(e) {
    list(error = conditionMessage(e))
  })
}

#* Find comparable pitchers by movement profile
#* Expects JSON body: pitch_type, ivb, hb_sym, velo, arm_angle, p_throws
#* Returns top 5 closest pitchers with their full arsenals
#* @post /find_comps
#* @serializer unboxedJSON
function(req) {
  tryCatch({

    # ── Guard: profiles must have loaded at startup ──────────────────────────
    if (is.null(pitcher_profiles)) {
      return(list(error = "pitcher_profiles.csv failed to load at startup"))
    }

    # ── Parse request body ───────────────────────────────────────────────────
    body <- fromJSON(req$postBody, simplifyVector = TRUE)

    input_pitch_type <- as.character(body$pitch_type)
    input_p_throws   <- as.character(body$p_throws)
    input_ivb        <- as.numeric(body$ivb)
    input_hb         <- as.numeric(body$hb_sym)
    input_velo       <- as.numeric(body$velo)
    input_arm        <- as.numeric(body$arm_angle)

    # ── Filter to matching anchor pitch type (any handedness β€” hb_sym is already symmetrized)
    anchor_rows <- pitcher_profiles[pitch_type == input_pitch_type]

    if (nrow(anchor_rows) == 0) {
      return(list(error = "no matches"))
    }

    # ── Compute weighted distance for each matching row ──────────────────────
    # Arm angle tightly weighted (/8) so a submarine pitcher never matches an over-the-top guy.
    # If input arm_angle is NA/null, skip that term to avoid NaN distances.
    arm_term <- if (!is.na(input_arm)) ((anchor_rows$arm_angle - input_arm) / 8)^2 else 0
    anchor_rows[, dist := sqrt(
      ((ivb       - input_ivb)  / 3)^2 +
      ((hb_sym    - input_hb)   / 3)^2 +
      ((velo      - input_velo) / 5)^2 +
      arm_term
    )]

    # ── Top 5 closest pitchers ───────────────────────────────────────────────
    top5 <- head(anchor_rows[order(dist)], 5)

    # ── For each top pitcher, pull their full arsenal ────────────────────────
    result <- lapply(seq_len(nrow(top5)), function(i) {
      row         <- top5[i]
      pitcher_nm  <- row$player_name
      anchor_dist <- round(row$dist, 3)

      arsenal_rows <- pitcher_profiles[player_name == pitcher_nm]

      arsenal <- lapply(seq_len(nrow(arsenal_rows)), function(j) {
        r <- arsenal_rows[j]
        list(
          pitch_type = r$pitch_type,
          velo       = r$velo,
          ivb        = r$ivb,
          hb_sym     = r$hb_sym,
          rel_z      = r$rel_z,
          arm_angle  = r$arm_angle,
          n          = r$n
        )
      })

      list(
        player_name = pitcher_nm,
        p_throws    = row$p_throws,
        anchor_dist = anchor_dist,
        arsenal     = arsenal
      )
    })

    result

  }, error = function(e) {
    list(error = conditionMessage(e))
  })
}

#* Health check
#* @get /health
function() {
  list(status = "ok", model = "fit_l6_champ", time = as.character(Sys.time()))
}