the fine print (this isn’t a legally binding agreement)
So since I didn’t sign anything, this isn’t a legally binding agreement. I’m being exploited. I gave a similar model to Taylor Smith, who didn’t pay me enough to live in the US and I had to leave back to Colombia.
I am so broke, that the cost of the attorney would be more money than what I have. I am being exploited by millionaires. I have nothing to lose.
There are common econometrics models. Try agentic AI or use your brain. I got this from Andrew Gelman, anyway. They’re open source econometrics models, you retards.
This is what Sam Saskin from the New York Mets sent me for an “application” for a senior data scientist position.
Thank you for applying for the Data Scientist, Baseball Analytics position with the New York Mets. As part of the next step in our application, we ask that you submit responses to the following questions. Responses should be submitted in a zip file containing any text, outputs, and code used to answer the questions. Note, these are all hypothetical situations. Neither receiving nor responding to this questionnaire guarantees you a position with the New York Mets or an interview for any position, and nothing herein shall be construed as an offer of employment. You agree that you
will not be entitled to any compensation or credit because of the use by the New York Mets or any of its designees of any concepts, ideas, or material similar or identical to those stated in your responses to this questionnaire, and you waive all rights and claims relating to such
concepts, ideas, and material. In addition, you also agree to not publish or distribute these materials, questions, or your responses in any public forum, including the internet.
And here’s the HTML notebook, I sent The Mets. I did this on a timer and stayed up all night while moving.
And that’s a similar model I send the Rays.
Next, Here’s the Stan code for (one of) the models I sent the Rays:
data { int N; // N observations int T; // time points int L; // num levels int B; // num batters real y[N]; // outcome int<lower=1, upper=L> level[N]; int<lower=1, upper=T+2> time[N]; int<lower=1, upper=B> batter[N]; matrix[L, L] ss_cov_mu_b_T; matrix[L, L] ss_cov_mu_b_walk; matrix[B, B] ss_cov_mu_b_T_B; matrix[B, B] ss_cov_mu_b_walk_B;}transformed data { cholesky_factor_cov[L] cholesky_ss_cov_mu_b_T; cholesky_factor_cov[L] cholesky_ss_cov_mu_b_walk; cholesky_factor_cov[B] cholesky_ss_cov_mu_b_T_B; cholesky_factor_cov[B] cholesky_ss_cov_mu_b_walk_B; cholesky_ss_cov_mu_b_T = cholesky_decompose(ss_cov_mu_b_T); cholesky_ss_cov_mu_b_walk = cholesky_decompose(ss_cov_mu_b_walk); cholesky_ss_cov_mu_b_T_B = cholesky_decompose(ss_cov_mu_b_T_B); cholesky_ss_cov_mu_b_walk_B = cholesky_decompose(ss_cov_mu_b_walk_B);}parameters { vector[L] mu_b_prior; vector[B] mu_b_prior_B; vector[L] raw_mu_b_T; matrix[L, T] raw_mu_b; vector[B] raw_mu_b_T_B; matrix[B, T] raw_mu_b_B; real<lower=0> sigma;}transformed parameters { matrix[L, T] mu_b; vector[N] pi; matrix[B, T] mu_b_B; mu_b[:,T] = cholesky_ss_cov_mu_b_T * raw_mu_b_T + mu_b_prior; mu_b_B[:,T] = cholesky_ss_cov_mu_b_T_B * raw_mu_b_T_B + mu_b_prior_B; for (i in 1:(T-1)) { mu_b[:, T - i] = cholesky_ss_cov_mu_b_walk * raw_mu_b[:, T - i] + mu_b[:, T + 1 - i]; mu_b_B[:, T - i] = cholesky_ss_cov_mu_b_walk_B * raw_mu_b_B[:, T - i] + mu_b_B[:, T + 1 - i]; } for (i in 1:N) { pi[i] = mu_b[level[i], time[i]] + mu_b_B[batter[i], time[i]]; }}model { raw_mu_b_T ~ normal(0, 1); raw_mu_b_T_B ~ normal(0, 1); to_vector(raw_mu_b) ~ normal(0, 1); to_vector(raw_mu_b_B) ~ normal(0, 1); sigma ~ normal(0, 1); mu_b_prior ~ normal(0, 1); mu_b_prior_B ~ normal(0, 1); y ~ normal(pi, sigma);}generated quantities { real batter_preds[B, L, T]; matrix[L, T] batter1_pred; for (l in 1:L) { batter1_pred[l, 1:T] = mu_b[l, 1:T] + mu_b_B[1, 1:T]; } for (b in 1:B) { for (l in 1:L) { for (t in 1:T) { batter_preds[b, l, t] = mu_b[l, t] + mu_b_B[b, t]; } } }}
Here’s the PDFs for both programming tests:
I’m being exploited by billionaires.
And to make you look even more stupid:
THESE ARE OPEN SOURCE MODELS. HERE’S THE MODEL. ALL YOU HAD TO DO WAS LOOK IT UP. ANDREW FITS THESE EVERY YEAR.
data{ int N_national_polls; // Number of polls int N_state_polls; // Number of polls int T; // Number of days int S; // Number of states (for which at least 1 poll is available) + 1 int P; // Number of pollsters int M; // Number of poll modes int Pop; // Number of poll populations int<lower = 1, upper = S + 1> state[N_state_polls]; // State index int<lower = 1, upper = T> day_state[N_state_polls]; // Day index int<lower = 1, upper = T> day_national[N_national_polls]; // Day index int<lower = 1, upper = P> poll_state[N_state_polls]; // Pollster index int<lower = 1, upper = P> poll_national[N_national_polls]; // Pollster index //int<lower = 1, upper = M> poll_mode_state[N_state_polls]; // Poll mode index //int<lower = 1, upper = M> poll_mode_national[N_national_polls]; // Poll mode index //int<lower = 1, upper = Pop> poll_pop_state[N_state_polls]; // Poll mode index //int<lower = 1, upper = Pop> poll_pop_national[N_national_polls]; // Poll mode index int n_democrat_national[N_national_polls]; int n_two_share_national[N_national_polls]; int n_democrat_state[N_state_polls]; int n_two_share_state[N_state_polls]; vector<lower = 0, upper = 1.0>[N_national_polls] unadjusted_national; vector<lower = 0, upper = 1.0>[N_state_polls] unadjusted_state; // cov_matrix[S] ss_cov_mu_b_walk; // cov_matrix[S] ss_cov_mu_b_T; // cov_matrix[S] ss_cov_poll_bias; //*** prior input vector[S] mu_b_prior; vector[S] state_weights; real sigma_c; //real sigma_m; //real sigma_pop; real sigma_measure_noise_national; real sigma_measure_noise_state; //real sigma_e_bias; // covariance matrix and scales cov_matrix[S] state_covariance_0; real random_walk_scale; real mu_b_T_scale; real polling_bias_scale;}transformed data { real national_cov_matrix_error_sd = sqrt(transpose(state_weights) * state_covariance_0 * state_weights); cholesky_factor_cov[S] cholesky_ss_cov_poll_bias; cholesky_factor_cov[S] cholesky_ss_cov_mu_b_T; cholesky_factor_cov[S] cholesky_ss_cov_mu_b_walk; // scale covariance matrix[S, S] ss_cov_poll_bias = state_covariance_0 * square(polling_bias_scale/national_cov_matrix_error_sd); matrix[S, S] ss_cov_mu_b_T = state_covariance_0 * square(mu_b_T_scale/national_cov_matrix_error_sd); matrix[S, S] ss_cov_mu_b_walk = state_covariance_0 * square(random_walk_scale/national_cov_matrix_error_sd); // transformation cholesky_ss_cov_poll_bias = cholesky_decompose(ss_cov_poll_bias); cholesky_ss_cov_mu_b_T = cholesky_decompose(ss_cov_mu_b_T); cholesky_ss_cov_mu_b_walk = cholesky_decompose(ss_cov_mu_b_walk);}parameters { vector[S] raw_mu_b_T; matrix[S, T] raw_mu_b; vector[P] raw_mu_c; //vector[M] raw_mu_m; //vector[Pop] raw_mu_pop; //real<offset=0, multiplier=0.02> mu_e_bias; //real<lower = 0, upper = 1> rho_e_bias; //vector[T] raw_e_bias; vector[N_national_polls] raw_measure_noise_national; vector[N_state_polls] raw_measure_noise_state; vector[S] raw_polling_bias; // real mu_b_T_model_estimation_error;}transformed parameters { //*** parameters matrix[S, T] mu_b; vector[P] mu_c; //vector[M] mu_m; //vector[Pop] mu_pop; //vector[T] e_bias; vector[S] polling_bias = cholesky_ss_cov_poll_bias * raw_polling_bias; vector[T] national_mu_b_average; real national_polling_bias_average = transpose(polling_bias) * state_weights; //real sigma_rho; //*** containers vector[N_state_polls] logit_pi_democrat_state; vector[N_national_polls] logit_pi_democrat_national; //*** construct parameters mu_b[:,T] = cholesky_ss_cov_mu_b_T * raw_mu_b_T + mu_b_prior; // * mu_b_T_model_estimation_error for (i in 1:(T-1)) mu_b[:, T - i] = cholesky_ss_cov_mu_b_walk * raw_mu_b[:, T - i] + mu_b[:, T + 1 - i]; national_mu_b_average = transpose(mu_b) * state_weights; mu_c = raw_mu_c * sigma_c; //mu_m = raw_mu_m * sigma_m; //mu_pop = raw_mu_pop * sigma_pop; //e_bias[1] = raw_e_bias[1] * sigma_e_bias; //sigma_rho = sqrt(1-square(rho_e_bias)) * sigma_e_bias; //for (t in 2:T) e_bias[t] = mu_e_bias + rho_e_bias * (e_bias[t - 1] - mu_e_bias) + raw_e_bias[t] * sigma_rho; //*** fill pi_democrat for (i in 1:N_state_polls){ logit_pi_democrat_state[i] = mu_b[state[i], day_state[i]] + mu_c[poll_state[i]] + //mu_m[poll_mode_state[i]] + //mu_pop[poll_pop_state[i]] + //unadjusted_state[i] * e_bias[day_state[i]] + raw_measure_noise_state[i] * sigma_measure_noise_state + polling_bias[state[i]]; } logit_pi_democrat_national = national_mu_b_average[day_national] + mu_c[poll_national] + //mu_m[poll_mode_national] + //mu_pop[poll_pop_national] + //unadjusted_national .* e_bias[day_national] + raw_measure_noise_national * sigma_measure_noise_national + national_polling_bias_average; }model { //*** priors raw_mu_b_T ~ std_normal(); // student_t(4,0,1); // std_normal(); //mu_b_T_model_estimation_error ~ scaled_inv_chi_square(7, 1); to_vector(raw_mu_b) ~ std_normal(); raw_mu_c ~ std_normal(); //raw_mu_m ~ std_normal(); //raw_mu_pop ~ std_normal(); //mu_e_bias ~ normal(0, 0.02); //rho_e_bias ~ normal(0.7, 0.1); //raw_e_bias ~ std_normal(); raw_measure_noise_national ~ std_normal(); raw_measure_noise_state ~ std_normal(); raw_polling_bias ~ std_normal(); // student_t(4,0,1); // std_normal(); //*** likelihood n_democrat_state ~ binomial_logit(n_two_share_state, logit_pi_democrat_state); n_democrat_national ~ binomial_logit(n_two_share_national, logit_pi_democrat_national);}generated quantities { matrix[T, S] predicted_score; for (s in 1:S){ //predicted_score[1:T, s] = inv_logit(mu_a[1:T] + to_vector(mu_b[s, 1:T])); predicted_score[1:T, s] = inv_logit(to_vector(mu_b[s, 1:T])); }}
likely llc 
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