Expert Opinion: Moreton City Excelsior II vs Souths United

The upcoming match between Moreton City Excelsior II and Souths United promises to be an exciting encounter, with both teams showing strong potential to deliver a high-scoring affair. Based on the available data, the average total goals predicted for this match is 4.80, indicating a likely offensive display from both sides. With Moreton City Excelsior II having an average of 3.25 goals scored per game and Souths United conceding an average of 2.75 goals, it’s reasonable to anticipate a match filled with opportunities and dynamic play. The betting odds further support this expectation, particularly with the high probability of over 1.5 and over 2.5 goals.

Betting Predictions

Goals-Based Predictions

• Over 1.5 Goals: 97.90 – This high probability suggests that at least two goals will be scored in the match, aligning with the teams’ offensive capabilities.
• Over 2.5 Goals: 91.60 – A strong indication that the match will see at least three goals, reflecting the attacking nature of both teams.
• Over 3.5 Goals: 62.50 – While slightly lower, there’s still a significant chance for four or more goals, making it a viable bet for those seeking higher returns.

Scoreline Predictions

• Both Teams Not To Score In 1st Half: 87.40 – This suggests that while both teams are likely to score, they may do so later in the game.
• Both Teams To Score: 72.10 – A reasonable expectation given both teams’ ability to find the back of the net.
• Home Team To Win: 73.40 – Reflects a balanced competition, but with a slight edge towards Moreton City Excelsior II at home.

Second Half Predictions

• Both Teams Not To Score In 2nd Half: 63.50 – Indicates that while the first half may be goal-rich, the second half could see a slowdown in scoring.
• Home Team To Score In 2nd Half: 59.10 – Suggests Moreton City Excelsior II may capitalize on their home advantage in the latter stages of the game.

Additionadavid-merino/StackoverflowAPI/src/Components/StackOverflow/Question/Question.js
import React from ‘react’;
import QuestionDetails from ‘./QuestionDetails’;

function Question({ question }) {
const { title, link, tags } = question;
return (

{title}

{tags.map(tag => `#${tag}`)}

View Question

);
}

export default Question;david-merino/StackoverflowAPI/src/Components/StackOverflow/Questions.js
import React from ‘react’;
import Question from ‘./Question/Question’;
import Loading from ‘../Loading’;
import Error from ‘../Error’;

function Questions({ questions }) {
if (questions.loading) {
return (

);
}
else if (questions.error) {
return (

);
}
else if (questions.items.length === 0) {
return (

No Questions Found

);
}
else {
return (

{questions.items.map(question => (

))}
# Stackoverflow API

This project was bootstrapped with [Create React App](https://github.com/facebook/create-react-app).

## Available Scripts

In the project directory, you can run:

### `npm start`

Runs the app in the development mode.
Open [http://localhost:3000](http://localhost:3000) to view it in the browser.

The page will reload if you make edits.
You will also see any lint errors in the console.

### `npm test`

Launches the test runner in the interactive watch mode.
See the section about [running tests](https://facebook.github.io/create-react-app/docs/running-tests) for more information.

### `npm run build`

Builds the app for production to the `build` folder.
It correctly bundles React in production mode and optimizes the build for the best performance.

The build is minified and the filenames include the hashes.
Your app is ready to be deployed!

See the section about [deployment](https://facebook.github.io/create-react-app/docs/deployment) for more information.

### `npm run eject`

**Note: this is a one-way operation. Once you `eject`, you can’t go back!**

If you aren’t satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project.

Instead, it will copy all the configuration files and transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you’re on your own.

You don’t have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn’t feel obligated to use this feature. However we understand that this tool wouldn’t be useful if you couldn’t customize it when you are ready for it.

## Assignment

### Stackoverflow API

Use [this API](https://api.stackexchange.com/docs/questions) to create an application that allows users to search for questions by keyword.


#### User Stories

– As a user I should be able to search questions by keyword.
– As a user I should see loading feedback when waiting for results.
– As a user I should see an error message if something goes wrong.
– As a user I should see all results paginated by ten items per page.
– As a user I should see details about each question including:
– Title
– Tags
– Link to question

#### Technical Requirements

– The project must use React.
– The project must use create-react-app.
– The project must use functional components.
– The project must use hooks.
– The project must use react-router.
– The project must use axios or fetch.
david-merino/StackoverflowAPI/src/App.js
import React from ‘react’;
import ‘./App.css’;
import Search from ‘./Components/Search/Search’;
import { BrowserRouter as Router, Route } from ‘react-router-dom’;
import Questions from ‘./Components/StackOverflow/Questions’;

function App() {
return (

david-merino/StackoverflowAPI/src/index.js
import React from ‘react’;
import ReactDOM from ‘react-dom’;
import ‘./index.css’;
import App from ‘./App’;
import reportWebVitals from ‘./reportWebVitals’;

const stackoverflowAPI = “https://api.stackexchange.com/2.3/search”;
const site = “stackoverflow”;

ReactDOM.render(

{/* Context Provider */}

import React from ‘react’;

function Loading() {
return (

Loading…

{/* {loading && “Loading…”} */}
lauramcclure/FSI_MRI_NLP/scripts/fsl_statistical_analysis.py
#!/usr/bin/env python

“””
This script contains functions for running fsl statistical analyses on preprocessed MRI data.

author: Laura McClure
email: [email protected]
“””

# import standard libraries
from os import system

# import scientific computing libraries
from nipype.interfaces.fsl import FEATModel

def make_design_matrix(subjects_list_file,
output_dir,
TR=1,
high_pass_cutoff=128,
run_num=’all’,
model_type=’AR(1)’,
model_variance=’constant’,
motion_parameters=[‘motion_parameters.txt’],
covariates=[],
output=’zstat’):
“””
This function runs FEATModel (from FSL) on preprocessed functional MRI data and generates design matrices based on
input parameters (TR, high-pass cutoff frequency). It outputs design matrices for each subject specified in subjects_list_file.
Parameters:
subjects_list_file (str): path to text file containing list of subjects used for analysis; each subject should be on its own line
output_dir (str): path to output directory where design matrices will be saved
TR (float): repetition time; default = 1 sec; if TR != 1 sec, enter TR as float value
high_pass_cutoff (float): high-pass cutoff frequency; default = 128 sec; if high-pass cutoff frequency != 128 sec, enter value as float
run_num (int): run number of interest; default = ‘all’; if run_num != ‘all’, enter as int value corresponding to desired run number
model_type (str): type of noise model; options include AR(1), ARMA(1), Gaussian; default = ‘AR(1)’
model_variance (str): type of variance model; options include constant or unstructured; default = ‘constant’
motion_parameters (list): list of motion parameters; default = [‘motion_parameters.txt’]; enter as list object with strings corresponding
to paths of motion parameters file(s)
covariates (list): list of covariates; default = []; enter as list object with strings corresponding
to paths of covariate file(s)
output (str): type of output requested; options include zstat or tfce; default = ‘zstat’

Returns:
NoneType
“””

with open(subjects_list_file,’r’) as subjects_list:

for subject in subjects_list:
subject=subject.rstrip(‘n’)

if run_num==’all’:
for i in range(1,int(len(motion_parameters)/len(covariates))+1):
system(“feat_preproc.py –cope0 %s/%s_func_run%i_preproc.nii.gz –cope0var %s/%s_func_run%i_preproc_var.nii.gz –mask %s/%s_func_run%i_preproc_mask.nii.gz –fwhm %i –maskthresh %f –regressors %s/%s_func_run%i_preproc_motion_parameters.txt –output_dir %s/design_matrices/%s/run%i” %(output_dir,
subject,
i,
output_dir,
subject,
i,
output_dir,
subject,
i,
int(TR*6),
float(TR*0.15),
output_dir,
subject,
i,
output_dir,
output_dir,
subject,
i))
system(“feat_design.py –copescope %i –cope0varcope0 %i –maskcope0 %i –featmodel=%s,%s –featmodelvar=%i,%i –output_dir %s/design_matrices/%s/run%i” %(output_dir,
output_dir,
output_dir,
model_type,
model_variance,
output_dir,
output_dir,
output_dir,
output_dir,
subject,i))
system(“feat_regfilt.py –in=%s/design_matrices/%s/run%i/design.fsf –out=%s/design_matrices/%s/run%i/design_with_covariates.fsf –copescope %i –cope0varcope0 %i –maskcope0 %i” %(output_dir,output_dir,subject,i,output_dir,output_dir,i,output_dir,i,output_dir,i))
for j in range(len(covariates)):
system(“feat_regfilt.py –in=%s/design_matrices/%s/run%i/design_with_covariates.fsf –out=%s/design_matrices/%s/run%i/design_with_covariates.fsf –covarcope%d %s/%s_func_run%i_preproc_%scovariate.txt” %(output_dir,output_dir,i,output_dir,output_dir,i,j+1,output_dir,output_dir,j+1,covariates[j]))
system(“feat_regfilt.py –in=%s/design_matrices/%s/run%i/design_with_covariates.fsf –out=%s/design_matrices/%s/run%i/design_with_covariates.fsf –autocorrelate” %(output_dir,output_dir,i,output_dir,output_dir,i))
system(“feat_finish.py –fsf=%s/design_matrices/%s/run%i/design_with_covariates.fsf –highpass=%f” %(output_dir,output_dir,i,output))
system(“feat_poststats.py –copestatscope=%i –copestatscopevar=%i –maskcopestatscope=%i” %(output_dir,output_dir,output_dir))

else:
for i in range(1,len(motion_parameters)/len(covariates)+1):
if i==run_num:
system(“feat_preproc.py –cope0 %s/%s_func_run%i_preproc.nii.gz –cope0var %s/%s_func_run%i_preproc_var.nii.gz –mask %s/%s_func_run%i_preproc_mask.nii.gz –fwhm %i –maskthresh %f –regressors %s/%s_func_run%i_preproc_motion_parameters.txt –output_dir %s/design_matrices/%s/run%i” %(output_dir,
subject,
i,
output_dir,
subject,
i,
output_dir,
subject,
i,
int(TR*6),
float(TR*0.15),
output_dir,
subject,
i,
output_dir,
output_dir,
subject,i))
system(“feat_design.py –copescope %i –cope0varcope0 %i –maskcope0 %i –featmodel=%s,%s –featmodelvar=%i,%i –output_dir %s/design_matrices/%s/run%i” %(output_dir,output_dir,output_dir,model_type,model_variance,output_dir,output_dir,output_dir,output_dir,i))
system(“feat_regfilt.py –in=%s/design_matrices/%s/run%i/design.fsf –out=%s/design_matrices/%s/run%i/design_with_covariates.fsf” %(output_dir,output_dir,i,output_dir,output_dir,i))
for j in range(len(covariates)):
system(“feat_regfilt.py –in=%s/design_matrices/%s/run%i/design_with_covariates.fsf –out=%s/design_matrices/%s/run%i/design_with_covariates.fsf” %(output_DIR=output_DIR,SUBJECT=subject,RUN=i,COVARIATE=j+1,COVARIATE_FILE=covariates[j]))
system(“feat_regfilt.py feat_regfilt.py feat_regfilt.py feat_regfilt.py feat_regfilt.py feat_regfilt.py feat_finish_feat_finish_feat_finish_feat_finish_feat_finish_feat_finish.py”)
system(“feat_finish.py”)
system(“feat_poststats.py”)

return None

def run_feat(subject_list_file=”,
analysis_name=”,
contrast_def_file=”,
output_type=’zstat’,
threshold=’none’,
threshold_option=’height’,
height_threshold=2.3):
“””
This function runs FEATModel (from FSL) on preprocessed functional MRI data using design matrices generated using make_design_matrix().
It outputs statistical maps based on input parameters (contrast def file).
It can also threshold these maps using either z-score or TFCE thresholding methods.
Parameters:
subject_list_file (str): path to text file containing list of subjects used for analysis; each subject should be on its own line
analysis_name (str): name given to current analysis
contrast_def_file (str): path to contrast definition file specifying contrasts analyzed using FEATModel
output_type (str): type of output requested; options include zstat or tfce; default = ‘zstat’
threshold (str): type of thresholding requested; options include none or height; default = ‘none’
threshold_option (str): type of thresholding method requested when threshold=’height’; options include height or extent; default = ‘height’
height_threshold (float): height threshold value used when threshold_option=’height’; default = 2.3

Returns:
NoneType

Output Files:
The following files are saved within directories named after each subject specified in subject_list_file:

feat_model_output.txt: summary table listing statistical map p-values associated with contrasts specified in contrast_def_file

feat_model_output.pdf: figure displaying statistical maps associated with contrasts specified in contrast_def_file

feat_model_output_thresh.txt: summary table listing statistical map p-values associated with contrasts specified in contrast_def_file after thresholding

feat_model_output_thresh.pdf: figure displaying statistical maps associated with contrasts specified in contrast_def_file after thresholding

statistical_maps.nii.gz: nifti image containing statistical maps associated with contrasts specified in contrast_def_file

statistical_maps_thresh.nii.gz: nifti image containing statistical maps associated with contrasts specified in contrast_def_file after threshold