Mount Pleasant Academy FC: A Comprehensive Guide for Sports Bettors

Overview / Introduction about the Team

Mount Pleasant Academy FC is a prominent football team based in Mount Pleasant, competing in the Premier League. The team plays with a 4-3-3 formation and is managed by Coach John Smith. Founded in 1985, the club has established itself as a formidable force in the league.

Team History and Achievements

Over the years, Mount Pleasant Academy FC has claimed several titles, including two league championships and three cup victories. Notable seasons include their 2005 campaign, where they finished top of the league table. The club also holds records for the most consecutive wins in a season.

Current Squad and Key Players

The current squad boasts key players like striker David Johnson, midfielder Alex Brown, and goalkeeper Chris Lee. David Johnson is known for his goal-scoring prowess, while Alex Brown excels in playmaking from midfield.

Team Playing Style and Tactics

The team employs a 4-3-3 formation focusing on high pressing and quick transitions. Their strengths lie in their attacking play and solid defense, though they occasionally struggle with maintaining possession under pressure.

Interesting Facts and Unique Traits

Nicknamed “The Eagles,” Mount Pleasant Academy FC has a passionate fanbase known as “Eagle Eyes.” They have a fierce rivalry with Rivertown Rovers and are known for their pre-match tradition of playing “Eye of the Tiger.”

Lists & Rankings of Players, Stats, or Performance Metrics

• Top Scorer: David Johnson (✅)
• Pivotal Midfielder: Alex Brown (💡)
• Betworthy Player: Chris Lee (🎰)

Comparisons with Other Teams in the League or Division

In comparison to other teams like Rivertown Rovers, Mount Pleasant Academy FC often outperforms them in terms of goals scored per game but sometimes falls short defensively.

Case Studies or Notable Matches

A breakthrough game was their 2010 match against City United where they secured an unexpected victory through a last-minute goal by David Johnson.

Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds

Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks

• Analyze recent form to predict performance trends.
• Bet on David Johnson to score when odds are favorable.
• Closely watch matchups against Rivertown Rovers due to historical rivalry intensity.

Quotes or Expert Opinions about the Team (Quote Block)

“Mount Pleasant Academy FC’s dynamic playstyle makes them unpredictable yet exciting to watch,” says sports analyst Jane Doe.

Pros & Cons of the Team’s Current Form or Performance ✅❌ Lists

• ✅ Strong attacking lineup with high goal conversion rate.
• ❌ Defensive lapses leading to unnecessary goals conceded.

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#!/usr/bin/env python [1]: # Copyright (c) Facebook, Inc. and its affiliates. [2]: # This source code is licensed under the MIT license found in the [3]: # LICENSE file in the root directory of this source tree. [4]: import argparse [5]: import logging [6]: import os [7]: import sys [8]: from collections import defaultdict [9]: import numpy as np [10]: logger = logging.getLogger(__name__) [11]: def parse_args(): [12]: parser = argparse.ArgumentParser( [13]: description=’Generate summary statistics for given set of results’ [14]: ) [15]: parser.add_argument( [16]: ‘–results_dir’, [17]: type=str, [18]: required=True, [19]: help=’Directory containing results’, [20]: ) [21]: parser.add_argument( [22]: ‘–result_file’, [23]: type=str, [24]: default=’results.jsonl’, [25]: help=’Results file name within each experiment directory.’, [26]: ) def get_results_dirs(results_dir): if os.path.isdir(results_dir): return [results_dir] else: return [ os.path.join(results_dir, d) for d in os.listdir(results_dir) if os.path.isdir(os.path.join(results_dir,d)) ] def get_result_files(result_dirs): result_files = [] for result_dir in result_dirs: result_file_path = os.path.join(result_dir,result_file) if not os.path.exists(result_file_path): logger.error(f’Could not find {result_file} at {result_dir}’) continue result_files.append(result_file_path) return result_files def read_results(result_files): results = defaultdict(list) num_read = [] num_skipped = [] total_num_read = [] total_num_skipped = [] total_num_entries = [] logger.info(‘Reading results…’) logger.info(f’Reading {len(result_files)} files’) for i,result_file_path in enumerate(result_files): logger.info(f’Reading file {i+1}/{len(result_files)}’) with open(result_file_path,’r’) as f: num_read_ = num_skipped_ = total_num_read_ = total_num_skipped_ = total_num_entries_ = n=0 for line_number,line_str in enumerate(f): n +=1 try: entry_dict = eval(line_str.strip()) results[‘dataset’].append(entry_dict[‘dataset’]) results[‘task’].append(entry_dict[‘task’]) results[‘model’].append(entry_dict[‘model’]) results[‘fold’].append(entry_dict[‘fold’]) results[‘split’].append(entry_dict[‘split’]) if ‘num_read’ not in entry_dict: entry_dict[‘num_read’] = -1 if ‘num_skipped’ not in entry_dict: entry_dict[‘num_skipped’] = -1 if ‘total_num_read’ not in entry_dict: entry_dict[‘total_num_read’] = -1 if ‘total_num_skipped’ not in entry_dict: entry_dict[‘total_num_skipped’] = -1 if ‘total_num_entries’ not in entry_dict: entry_dict[‘total_num_entries’] = -1 num_read_.append(entry_dict.get(‘num_read’,None)) num_skipped_.append(entry_dict.get(‘num_skipped’,None)) total_num_read_.append(entry_dict.get(‘total_num_read’,None)) total_num_skipped_.append(entry_dict.get(‘total_num_skipped’,None)) total_num_entries_.append(entry_dict.get(‘total_num_entries’,None)) except Exception as e: logger.warning(f’Error reading line {line_number} from {result_file_path}’) logger.exception(e) num_skipped_ += [None] num_read.append(num_read_) num_skipped.append(num_skipped_) total_num_read.append(total_num_read_) total_num_skipped.append(total_num_skipped_) total_num_entries.append(total_num_entries_) logger.info(f’Read {n} lines ({n-len(num_skiped_)}) valid entries’) logger.info(f’Skipping {len(num_skiped_)} entries’) print() print() print() print() print() print() print() print() num_reads_flat_list=[] num_skipeds_flat_list=[] total_reads_flat_list=[] total_skipeds_flat_list=[] tot_nums_flat_list=[] for i,result_file_path_i in enumerate(result_files): try: nread_i=num_reads[i][~np.isnan(num_reads[i])] nskip_i=num_skipeds[i][~np.isnan(num_skipeds[i])] treads_i=total_reads[i][~np.isnan(total_reads[i])] tskip_i=total_skipeds[i][~np.isnan(total_skipeds[i])] totnums_i=tot_nums[i][~np.isnan(tot_nums[i])] assert len(nread_i)==len(nskip_i)==len(treads_i)==len(tskip_i)==len(totnums_i),f'{nreadi},{nskipi},{treadsi},{tskipt},{totnums}’ num_reads_flat_list.extend(nread_i) num_skipeds_flat_list.extend(nskip_i) total_reads_flat_list.extend(treads_i) total_skipeds_flat_list.extend(tskip_i) tot_nums_flat_list.extend(totnums_i) fig=plt.figure(figsize=(10.,10.),dpi=300) ax=fig.add_subplot(111) ax.scatter(x=num_reads_flat_list,y=num_skipeds_flat_list,s=tot_nums_flat_list,c=np.log(np.array(total_reads_flat_list)),alpha=.75,cmap=’viridis’) plt.colorbar() plt.xlabel(‘# reads’) plt.ylabel(‘# skipped’) plt.title(‘Read/skip counts per dataset’) plt.savefig(os.path.join(args.results_dir,’scatter_plot.png’)) def compute_statistics(results,num_reads,num_skipeds,total_reads,total_skipeds,tot_nums): def _compute_stats(values,is_nan_allowed=False,min_n_valid=None,nan_value=None,**kwargs): values=np.array(values,dtype=float).flatten() nan_values=values[np.isnan(values)] values=values[np.logical_not(np.isnan(values))] stats=dict() stats.update(kwargs) stats.update({ ‘min’:float(np.min(values)), ‘max’:float(np.max(values)), ‘mean’:float(np.mean(values)), ‘median’:float(np.median(values)), ‘std’:float(np.std(values)), ‘var’:float(np.var(values)), ‘count’:int(len(values)), }) . if min_n_valid is None: min_n_valid=int(len(nan_values)/5.) if len(nan_values)>min_n_valid and not is_nan_allowed: raise ValueError(‘Too many NaN values’) elif len(nan_values)>min_n_valid: values=np.concatenate([values,np.full(len(nan_values),nan_value)]) stats_per_dataset=dict() datasets=set(results.get(‘dataset’)) stats_per_dataset.update({ dataset:_compute_stats([ float(a*b)/c for a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z] ) for x,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z ,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z ,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b ,c] ,d] ,e] ,f] ,g] ,h] ,i] ,j] ,k] ,l] ,m] ) ]) ]) ]) ]) ]) ]) ]) ]) ]) ]) ]) ]) ]) ]) ]) ],dataset,’fraction read’,is_nan_allowed=True,min_n_valid=len(datasets)-10) stats_per_dataset.update({ dataset:_compute_stats([ float(a) for a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z ] ],dataset,’fraction skipped’,is_nan_allowed=True,min_n_valid=len(datasets)-10) stats_per_dataset.update({ dataset:_compute_stats([ float(a*b)/c for a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z ] ],dataset,’fraction read skipped’,is_nan_allowed=True,min_n_valid=len(datasets)-10) stats_per_dataset.update({ dataset:_compute_stats([ float(b)/a for a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z ] ],dataset,’skips per read’,is_nan_allowed=True,min_n_valid=len(datasets)-10) stats_per_dataset.update({ dataset:_compute_stats([ float(c-a-b)/a for a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z ] ],dataset,’unseen fraction’,is_nan_allowed=True,min_n_valid=len(datasets)-10) stats_per_dataset.update({ dataset:_compute_stats([ float(d-c)/(c-a-b) for a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z ] ],dataset,’skips unseen fraction’,is_nan_allowed=True,min_n_valid=len(datasets)-10) return datasets,num_reads,num_skipeds,total_reads,total_skipeds,tot_nums,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,{} datasets,num_reads,num_skipeds,total_reads,total_skipeds, tot_nums,_stats_per_dataset,_stats_per_task,_stats_per_model, _stats_per_fold,_stats_per_split=_compute_statistics( results,num_reads,num_skipeds,total_reads,total_skipeds, tot_nums) fig=plt.figure(figsize=(12.,6.),dpi=300) ax=fig.add_subplot(121) ax.set_xscale(“log”) ax.set_yscale(“log”) ax.scatter(x=tot_nums.flatten(),y=np.array(total_reads).flatten(),alpha=.75) plt.xlabel(‘# examples’) plt.ylabel(‘# reads’) plt.title(‘Read counts per example count’) ax.set_xscale(“log”) ax.set_yscale(“log”) ax.scatter(x=tot_nums.flatten(),y=np.array(total_skipeds).flatten(),alpha=.75) plt.xlabel(‘# examples’) plt.ylabel(‘# skips’) plt.title(‘Skip counts per example count’) def _print_stats(stats,name=”,prefix=”): s=’n’+name+’n’ s+=prefix+’———————————n’ s+=prefix+’Meantt’+str(round(stats.get(‘mean’),4))+’n’ s+=prefix+’Stdtt’+str(round(stats.get(‘std’),4))+’n’ s+=prefix+’Mintt’+str(round(stats.get(‘min’),4))+’n’ s+=prefix+’Maxtt’+str(round(stats.get(‘max’),4))+’n’ s+=prefix+’Mediant’+str(round(stats.get(‘median’),4))+’n’ s+=prefix+’Countt’+str(int(stats.get(‘count’)))+’n’ return s fig=plt.figure(figsize=(12.,12.),dpi=300) ax=plt.subplot(321) stat_names=[‘fraction read’,’fraction skipped’,’fraction read skipped’,’skips per read’,’unseen fraction’,’skips unseen fraction’] stat_labels=[‘Fraction Read’,’% Skipped’,’% Read Skipped’,’Skips/Read’,’Unseen Fraction’,’Skips Unseen Fraction’] colors=[‘#ff6666′,’#66ff66′,’#6666ff’] max_vals=[100.,100.,100.,100.,100.,100.] ylabels=[‘Fraction Read’,’% Skipped’,’% Read Skipped’,’Skips/Read’,’Unseen Fraction’,’Skips Unseen Fraction’] hatches=[‘///’,’.//’,’.//’] hatches.reverse() hatch_styles=[]*len(stat_names) hatch_styles.append(hatches.copy()) hatch_styles.append(hatches.copy()) hatch_styles.append(hatches.copy()) axes=ax.ravel() axes[-1].set_visible(False) axes[-5].set_title(stat_labels[-5]) axes[-5].set_xlabel(stat_labels[-5]) axes[-5].set_ylabel(ylabels[-5]) axes[-5].set_ylim([0,max_vals[-5]]) i=-4 for j,(stat_name,color,max_val,label,axis_label)inenumerate(zip(stat_names, colors,max_vals,[stat_labels.index(s)+1 for s in stat_names],ylabels)): i-=1 axis=ax[j] axis.set_title(label) axis.set_xlabel(label) axis.set_ylabel(axis_label) tickvals=list(range(0,int(max_val+50),int(max_val/4))) tickvals.remove(0) tickvals=[tv if tv!=max_val else max_val-int(max_val/8) for tv in tickvals] hatches=hatch_styles[j] datasets=list(_stats_per_dataset.keys()) datasets.sort(key=lambda x:x.lower()) means=[round(_stats_per_dataset[d][‘mean’],4) + ‘t({})’.format(round(_stats_per_dataset[d][‘std’],4))