Connections: Sports Edition is a new version of the popular New York Times word game that seeks to test the knowledge of sports fans.
Like the original Connections, the game is all about finding the “common threads between words.” And just like Wordle, Connections resets after midnight and each new set of words gets trickier and trickier—so we’ve served up some hints and tips to get you over the hurdle.
If you just want to be told today’s puzzle, you can jump to the end of this article for the latest Connections solution. But if you’d rather solve it yourself, keep reading for some clues, tips, and strategies to assist you.
The NYT‘s latest daily word game has launched in association with The Athletic, the New York Times property that provides the publication’s sports coverage. Connections can be played on both web browsers and mobile devices and require players to group four words that share something in common.
Each puzzle features 16 words and each grouping of words is split into four categories. These sets could comprise of anything from book titles, software, country names, etc. Even though multiple words will seem like they fit together, there’s only one correct answer.
If a player gets all four words in a set correct, those words are removed from the board. Guess wrong and it counts as a mistake—players get up to four mistakes until the game ends.
Players can also rearrange and shuffle the board to make spotting connections easier. Additionally, each group is color-coded with yellow being the easiest, followed by green, blue, and purple. Like Wordle, you can share the results with your friends on social media.
Mashable Top Stories
Here’s a hint for today’s Connections Sports Edition categories
Want a hint about the categories without being told the categories? Then give these a try:
Yellow: Duke it out
Green: Alternative names for Massachusetts teams
Blue: Soccer team nicknames
Purple: Famous group of players on Houston’s baseball team
Featured Video For You
Connections: How to play and how to win
Here are today’s Connections Sports Edition categories
Need a little extra help? Today’s connections fall into the following categories:
Don’t feel down if you didn’t manage to guess it this time. There will be new Connections for you to stretch your brain with tomorrow, and we’ll be back again to guide you with more helpful hints.
1. This puzzle features connections between different sports teams, athletes, and events.
2. Look for common themes or relationships between the clues to help find the connections.
Answers:
1. New York Yankees – Derek Jeter
2. Los Angeles Lakers – Kobe Bryant
3. Green Bay Packers – Aaron Rodgers
4. Chicago Bulls – Michael Jordan
5. New England Patriots – Tom Brady
6. Pittsburgh Steelers – Terry Bradshaw
7. Golden State Warriors – Stephen Curry
8. Manchester United – David Beckham
9. Boston Red Sox – David Ortiz
10. Dallas Cowboys – Emmitt Smith
Tags:
NYT Connections Sports Edition, Connections #116, January 17, hints and answers, solve Connections #116, sports trivia, puzzle solving, game tips, crossword hints, daily challenge, NYT puzzle answers
Connections is the one of the most popular New York Times word games that’s captured the public’s attention. The game is all about finding the “common threads between words.” And just like Wordle, Connections resets after midnight and each new set of words gets trickier and trickier—so we’ve served up some hints and tips to get you over the hurdle.
If you just want to be told today’s puzzle, you can jump to the end of this article for today’s Connections solution. But if you’d rather solve it yourself, keep reading for some clues, tips, and strategies to assist you.
The NYT‘s latest daily word game has become a social media hit. The Times credits associate puzzle editor Wyna Liu with helping to create the new word game and bringing it to the publications’ Games section. Connections can be played on both web browsers and mobile devices and require players to group four words that share something in common.
Each puzzle features 16 words and each grouping of words is split into four categories. These sets could comprise of anything from book titles, software, country names, etc. Even though multiple words will seem like they fit together, there’s only one correct answer.
If a player gets all four words in a set correct, those words are removed from the board. Guess wrong and it counts as a mistake—players get up to four mistakes until the game ends.
Players can also rearrange and shuffle the board to make spotting connections easier. Additionally, each group is color-coded with yellow being the easiest, followed by green, blue, and purple. Like Wordle, you can share the results with your friends on social media.
Ready for the answers? This is your last chance to turn back and solve today’s puzzle before we reveal the solutions.
Drumroll, please!
The solution to today’s Connections #573 is…
What is the answer to Connections today
Crush into a compact shape: BALL, CRUMPLE, SCRUNCH, WAD
Fasteners: BUCKLE, CLIP, HOOK, SNAP
Mark as completed: CHECK, CROSS, STRIKE, TICK
Depicted in Dali’s “The Persistence of Memory”: ANT, BRANCH, CLOCK, MELTING
Don’t feel down if you didn’t manage to guess it this time. There will be new Connections for you to stretch your brain with tomorrow, and we’ll be back again to guide you with more helpful hints.
Are you struggling to solve the New York Times Connections puzzle #573? Look no further! Here are some hints and answers to help you crack the code:
Hints:
1. The theme of this puzzle is “Famous Duos.” Think about pairs of people who are commonly known to be associated with each other.
2. Some of the clues may reference movies, TV shows, or historical events where these duos are featured.
3. Pay attention to any clues that mention specific years or time periods, as they may help you narrow down your answers.
Answers:
1. Batman and Robin
2. Romeo and Juliet
3. Bonnie and Clyde
4. Sonny and Cher
5. Simon and Garfunkel
Keep these hints and answers in mind as you work through the puzzle. Good luck!
Tags:
NYT Connections, Connections puzzle, Connections hints, Connections answers, January 4 puzzle, NYT puzzle, NYT Connections tips, solve Connections puzzle, crossword puzzle, puzzle solving tips, word puzzle, word game, New York Times puzzle.
Connections is the one of the most popular New York Times word games that’s captured the public’s attention. The game is all about finding the “common threads between words.” And just like Wordle, Connections resets after midnight and each new set of words gets trickier and trickier—so we’ve served up some hints and tips to get you over the hurdle.
If you just want to be told today’s puzzle, you can jump to the end of this article for today’s Connections solution. But if you’d rather solve it yourself, keep reading for some clues, tips, and strategies to assist you.
The NYT‘s latest daily word game has become a social media hit. The Times credits associate puzzle editor Wyna Liu with helping to create the new word game and bringing it to the publications’ Games section. Connections can be played on both web browsers and mobile devices and require players to group four words that share something in common.
Each puzzle features 16 words and each grouping of words is split into four categories. These sets could comprise of anything from book titles, software, country names, etc. Even though multiple words will seem like they fit together, there’s only one correct answer.
If a player gets all four words in a set correct, those words are removed from the board. Guess wrong and it counts as a mistake—players get up to four mistakes until the game ends.
Players can also rearrange and shuffle the board to make spotting connections easier. Additionally, each group is color-coded with yellow being the easiest, followed by green, blue, and purple. Like Wordle, you can share the results with your friends on social media.
Ready for the answers? This is your last chance to turn back and solve today’s puzzle before we reveal the solutions.
Drumroll, please!
The solution to today’s Connections #573 is…
What is the answer to Connections today
Parts of a foot: ARCH, BALL, HEEL, SOLE
One dollar: BUCK, CLAM, SINGLE, SMACKER
Kinds of mushrooms: BUTTON, MOREL, OYSTER, TRUMPET
Pot ___: BELLY, HOLE, LUCK, STICKER
Don’t feel down if you didn’t manage to guess it this time. There will be new Connections for you to stretch your brain with tomorrow, and we’ll be back again to guide you with more helpful hints.
Welcome to our NYT Connections hints and answers for January 3! Today, we’ll be providing you with some tips to help you solve the puzzle for ‘Connections’ #572.
For those unfamiliar with the game, ‘Connections’ presents a series of clues that all have a common theme linking them together. Your task is to figure out what that theme is and connect the dots to solve the puzzle.
Here are some hints to get you started:
1. Look for common threads: As you read through the clues, try to identify any recurring motifs, keywords, or concepts that could point you towards the theme.
2. Think laterally: Don’t be afraid to think outside the box and make connections that might not be immediately obvious. Sometimes, the most unexpected links can lead to the solution.
3. Use the process of elimination: If you’re stuck on a particular clue, try ruling out potential themes that don’t fit with the other clues. This can help narrow down your options and steer you in the right direction.
And now, without further ado, here are the answers for ‘Connections’ #572:
1. Traffic lights
2. The Doors
3. The Beatles
4. The Rolling Stones
Did you manage to crack the code and uncover the hidden theme connecting these clues? Let us know in the comments below!
Stay tuned for more NYT Connections hints and answers coming your way soon. Happy puzzling!
Connections is the one of the most popular New York Times word games that’s captured the public’s attention. The game is all about finding the “common threads between words.” And just like Wordle, Connections resets after midnight and each new set of words gets trickier and trickier—so we’ve served up some hints and tips to get you over the hurdle.
If you just want to be told today’s puzzle, you can jump to the end of this article for today’s Connections solution. But if you’d rather solve it yourself, keep reading for some clues, tips, and strategies to assist you.
The NYT‘s latest daily word game has become a social media hit. The Times credits associate puzzle editor Wyna Liu with helping to create the new word game and bringing it to the publications’ Games section. Connections can be played on both web browsers and mobile devices and require players to group four words that share something in common.
Each puzzle features 16 words and each grouping of words is split into four categories. These sets could comprise of anything from book titles, software, country names, etc. Even though multiple words will seem like they fit together, there’s only one correct answer.
If a player gets all four words in a set correct, those words are removed from the board. Guess wrong and it counts as a mistake—players get up to four mistakes until the game ends.
Players can also rearrange and shuffle the board to make spotting connections easier. Additionally, each group is color-coded with yellow being the easiest, followed by green, blue, and purple. Like Wordle, you can share the results with your friends on social media.
Ready for the answers? This is your last chance to turn back and solve today’s puzzle before we reveal the solutions.
Drumroll, please!
The solution to today’s Connections #573 is…
What is the answer to Connections today
Perceive: CATCH, CLOCK, NOTICE, REGISTER
Cadence: BEAT, METER, RHYTHM, TIME
One in a Group of Twelve: DONUT, INCH, JUROR, MONTH
Dog___: DAYS, PADDLE, TAG, TIRED
Don’t feel down if you didn’t manage to guess it this time. There will be new Connections for you to stretch your brain with tomorrow, and we’ll be back again to guide you with more helpful hints.
Are you stuck on today’s New York Times Connections puzzle? Don’t worry, we’ve got you covered with some hints and answers to help you solve puzzle #573!
Hints:
1. The theme for today’s puzzle is famous movie quotes. Look for phrases that are recognizable from popular films.
2. Pay attention to the clues given for each word and think about how they might relate to the movie quotes.
Answers:
1. “You can’t handle the truth!” – A Few Good Men
2. “Here’s looking at you, kid.” – Casablanca
3. “I’ll be back.” – The Terminator
4. “I feel the need – the need for speed.” – Top Gun
5. “May the Force be with you.” – Star Wars
Use these hints and answers to guide you through solving today’s Connections puzzle. Happy puzzling!
Are you ready to take your machine learning skills to the next level? In our latest post, we’ll be exploring advanced machine learning techniques in Python that can help you solve complex data science problems.
With the increasing availability of data and the growing demand for data-driven insights, mastering advanced machine learning techniques has become essential for any data scientist or machine learning enthusiast. In this post, we’ll cover topics such as deep learning, reinforcement learning, natural language processing, and more, all using Python as our programming language of choice.
Whether you’re looking to enhance your career prospects, tackle challenging data science projects, or simply expand your knowledge and skills, this post is for you. Stay tuned as we delve into the world of advanced machine learning with Python and unlock the potential of cutting-edge techniques to solve real-world problems.
#Advanced #Machine #Learning #Python #Solve #data #science #problems #mastering #cuttingedge #machine #learning #techniques #Python,deep learning for nlp and speech recognition
David Stearns has steadily dropped breadcrumbs to his player procurement strategy.
In his year-plus with the Mets, Stearns has demonstrated that for players in their twenties with plenty of prime years remaining and a high ceiling, he will advocate to use Steve Cohen’s money aggressively.
Thus, last offseason, the Mets were willing to match the Dodgers’ 12-year, $325 million bid for 25-year-old Yoshinobu Yamamoto and would have gone higher had the pitcher’s reps not told them to stop because Yamamoto wanted to play with Shohei Ohtani in Los Angeles.
Pete Alonso helped the Mets reach the NLCS. Charles Wenzelberg / New York Post
This offseason the Mets agreed to the largest contract ever (years, signing bonus, total dollars) with Juan Soto, who turned 26 in October.
The New York Mets are facing a contract dilemma with star first baseman Pete Alonso. As Alonso continues to produce at an elite level, the Mets must decide how to handle his impending contract negotiations. Here are a few potential solutions and alternatives for the Mets to consider:
1. Lock Him Up Long-Term: The Mets could offer Alonso a lucrative long-term contract extension to keep him in New York for the foreseeable future. This would ensure that the Mets have a cornerstone player for years to come and provide stability at the first base position.
2. Arbitration: The Mets could choose to go through the arbitration process with Alonso, allowing for a shorter-term contract that would give the team more flexibility in the future. While this option may be more cost-effective in the short term, it could lead to tension between the player and the team.
3. Trade Him: If the Mets are unable to come to terms with Alonso on a contract extension, they could explore trading him to another team in exchange for a package of young, controllable players. This would allow the Mets to replenish their farm system while also addressing other needs on the roster.
4. Sign a Free Agent: If the Mets are unable to reach an agreement with Alonso, they could look to the free agent market to find a replacement at first base. While this option may not be as appealing as keeping Alonso, it could provide the team with a short-term solution while they continue to develop their prospects.
Ultimately, the Mets will need to carefully consider their options and weigh the potential risks and rewards of each decision. Whatever path they choose, it’s clear that the team will need to address the Pete Alonso contract situation sooner rather than later.
Price: $79.99 – $70.06 (as of Dec 31,2024 17:37:32 UTC – Details)
From the brand
Databases, data science & more
Sharing the knowledge of experts
O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.
Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.
Customers find the book provides a collection of examples that walk you through the stages of graph queries. They find the writing quality good and easy to follow for beginners. The book is also described as thoughtfully done and suitable for beginners.
AI-generated from the text of customer reviews
Graph data is a powerful tool that can be used to solve complex problems in a variety of industries. Whether you are a data scientist, software engineer, or business analyst, understanding how to apply graph thinking and graph technologies can help you uncover insights and make better decisions.
In this practitioner’s guide, we will explore the fundamentals of graph data and how it can be used to solve complex problems. We will cover topics such as graph theory, graph databases, and graph algorithms, and provide practical examples of how these concepts can be applied in real-world scenarios.
By the end of this guide, you will have a solid understanding of how to leverage graph data to solve complex problems and drive innovation in your organization. So let’s dive in and explore the world of graph thinking and graph technologies together.
#Practitioners #Guide #Graph #Data #Applying #Graph #Thinking #Graph #Technologies #Solve #Complex #Problems,principles of machine learning: the three perspectives
Reinforcement Learning for Finance: Solve Problems in Finance with CNN and RNN U
Price : 35.62
Ends on : N/A
View on eBay
Reinforcement Learning for Finance: Solve Problems in Finance with CNN and RNN
In the world of finance, making accurate predictions and decisions is crucial for success. With the rise of artificial intelligence and machine learning, reinforcement learning has emerged as a powerful tool for tackling complex financial problems.
Reinforcement learning is a type of machine learning that enables an agent to learn how to make decisions by interacting with its environment and receiving rewards or punishments based on its actions. By using reinforcement learning algorithms such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), financial institutions can automate trading strategies, optimize portfolios, and predict market trends with greater accuracy.
CNNs are well-suited for tasks that involve spatial data, such as analyzing stock prices or market trends. By processing and extracting features from historical financial data, CNNs can identify patterns and trends that can be used to make informed trading decisions.
On the other hand, RNNs are ideal for tasks that involve sequential data, such as predicting future stock prices or market movements. By analyzing time-series data and capturing temporal dependencies, RNNs can predict future trends and make real-time decisions based on current market conditions.
By combining the power of CNNs and RNNs with reinforcement learning, financial institutions can develop sophisticated trading algorithms that can adapt to changing market conditions and optimize investment strategies in real-time.
In conclusion, reinforcement learning with CNNs and RNNs has the potential to revolutionize the finance industry by enabling more accurate predictions, faster decision-making, and improved portfolio management. By leveraging the capabilities of these advanced technologies, financial institutions can stay ahead of the competition and achieve greater success in the ever-changing world of finance.
#Reinforcement #Learning #Finance #Solve #Problems #Finance #CNN #RNN
Reinforcement Learning for Finance: Solve Problems in Finance with CNN and Rnn
Price : 40.59
Ends on : N/A
View on eBay
Reinforcement Learning for Finance: Solve Problems in Finance with CNN and RNN
Reinforcement learning has gained significant attention in the field of finance for its ability to tackle complex problems and optimize decision-making processes. By combining convolutional neural networks (CNN) and recurrent neural networks (RNN), reinforcement learning can be used to solve a wide range of financial problems.
CNNs are typically used for image recognition and processing, but they can also be applied to financial data analysis. By feeding financial data into a CNN, the network can learn patterns and trends that can be used to make predictions or optimize trading strategies.
RNNs, on the other hand, are ideal for time-series data and sequential modeling. In finance, RNNs can be used to analyze historical market data, predict future price movements, and identify opportunities for trading.
By combining CNNs and RNNs with reinforcement learning algorithms, finance professionals can develop sophisticated models that can adapt to changing market conditions, optimize trading strategies, and maximize returns. These models can learn from past experiences, make decisions based on current market conditions, and continuously improve their performance over time.
Overall, reinforcement learning with CNNs and RNNs has the potential to revolutionize the way financial institutions analyze data, make decisions, and manage risks. By leveraging the power of deep learning and reinforcement learning, finance professionals can stay ahead of the curve and capitalize on opportunities in the ever-changing financial markets.
#Reinforcement #Learning #Finance #Solve #Problems #Finance #CNN #Rnn
Frequent Greta Gerwig collaborator Saoirse Ronan should certainly be a part of the Chronicles of Narnia reboot in some capacity, and a particular casting choice for her could simultaneously solve one of the series’ biggest story problems. The beloved Barbie director is delving further into the fantasy genre with Netflix’s Chronicles of Narnia reboot and is expected to write and direct at least two movies in this new series. Jason Isaacs’ recent tease about The Magician’s Nephew suggests that Gerwig may not be starting out with the Pevensie family, but she will likely get to them sooner or later.
However, Gerwig’s Narnia movies face many challenges, including and especially elements vital to the story of the sibling kings and queens of Narnia. Peter, Susan, Edmund, and Lucy are skillfully written as foils to each other while embarking on a magical journey of discovering Narnia, becoming its ideal rulers, returning to their childhoods, and bringing what they learned from Narnia back into their real lives. However, Susan has historically been regarded as having the most problematic story of the Pevensie siblings — yet Saoirse Ronan’s much-demanded casting for Narnia could greatly improve this character arc.
Casting Saoirse Ronan As Adult Susan Could Solve One Of Narnia’s Biggest Ending Problems
Ronan Could Do Justice To Susan If Portraying Her Later In Life, Long After Narnia
At one time, Saoirse Ronan would have been perfect to play Susan Pevensie, but has now aged out of the role, when Susan is in her late teens, at the oldest. However, Ronan could simply play an adult Susan, even in a very brief scene. Susan could be a very effective framing device for the Narnia movies through bookending scenes showing her in the real world, living a normal life, and reflecting on the past. While satisfying Lady Bird and Little Women fans, this could also be an avenue to fixing the problems with Susan’s Narnia story.
The perfect Mr. Tumnus for Greta Gerwig’s upcoming Chronicles of Narnia movie made himself abundantly clear in a critically acclaimed fantasy.
The fact that Susan never returns to Narnia after the events of Prince Caspian has long been a point of contention among readers. However, exploring her life after Narnia could be the first step to creating a stronger story for her. Additionally, it would be a brief but still weighty role for Ronan, deserving of her talents. Ronan could take on a different minor role in Narnia, but it might be a waste of the Oscar-nominated actress. However, Susan is one of the main characters and someone fans want to see more of, so even the shortest scenes with her would be worth casting Ronan.
Greta Gerwig’s Narnia Remake Must Address The Issues With Susan Pevensie’s Fate
Susan Has Always Needed A Better Ending In Narnia
In C. S. Lewis’ final Narnia novel, Susan’s siblings all return to Narnia without her. The reason for this is essentially summarized as Susan has become a shallow, flirtatious young woman. The result is that she no longer believes Narnia actually happened, that it was merely a game they played as children. More importantly, the text characterizes Susan as having grown into a sexual adult, presented as a negative trait throughout the series when the characters are all children. Romances that happen among heroic adult characters are usually out of sight, while the evil witches have notably sexual characterizations.
Of all the things that Netflix’s Narnia movies should consider changing from the source material, this plot point is one that they have an undeniable obligation to alter.
Of all the things that Netflix’s Narnia movies should consider changing from the source material, this plot point is one that they have an undeniable obligation to alter, should the series go on long enough to show this. Scenes with an adult Susan showing her denying Narnia exists as a way to be happy with her life in the real world while emphasizing that condemning her for having any sexuality is unfair would improve her ending while rendering a metatextual moment that engages with both the history of the franchise and the outdated culture possibly still being depicted.
Narnia’s Ending Issues Must Be Addressed Carefully In Greta Gerwig’s Remake
Netflix Probably Still Shouldn’t Make Too Many Big Changes To Narnia’s Story
However, veering too far away from Narnia‘s original ending could also upset fans of the books. The concept of one of the main characters never returning to Narnia does have some poetry in it, illustrating their differing personalities. It’s the why Susan never returns to Narnia that needs to be handled better. The Pevensies live out a very complicated scenario where their different lives and different ages in Narnia and England clash, and it is plausible character development that Susan’s fond memories of Narnia were colored by these jarring transitions.
The White Witch and the Lady of the Green Kirtle are among the Northern Witches, Narnia characters who need a backstory in Gerwig’s reboot movies.
Only a few scenes with Susan, skillfully portrayed by an actress such as Ronan, could fix her entire story. Her not coming back to Narnia doesn’t have to be a punishment; she can be a nuanced adult figure who has something to lose in the real world, and whose ending constitutes a bittersweet message about growing up and moving on. With unrivaled creatives like Greta Gerwig and Saoirse Ronan, it is by no means impossible for The Chronicles of Narnia to finally do Susan justice.
The Chronicles of Narnia is a fantasy franchise based on the seven-book series written by C.S. Lewis between 1950 and 1956. The series is set in the magical world of Narnia, where children from our world are transported to fulfill prophecies, battle evil forces, and restore peace under the guidance of Aslan, a mystical lion. The franchise has seen multiple adaptations, including a BBC television series in the late 1980s, three major Hollywood films between 2005 and 2010, and an upcoming reboot by Netflix, which has generated significant anticipation. The franchise is beloved for its rich allegorical storytelling, blending Christian themes with epic fantasy elements.
Created by
C.S. Lewis
Character(s)
Aslan
, Lucy Pevensie
, Peter Pevensie
, Edmund Pevensie
, Susan Pevensie
, Prince Caspian
, The White Witch
, Reepicheep
, Eustace Scrubb
, Mr. Tumnus
Video Game(s)
The Chronicles Of Narnia
Greta Gerwig’s Narnia Remake Could Solve A Big Susan Pevensie Problem With A Popular Casting Choice
Fans of C.S. Lewis’s beloved Chronicles of Narnia series have long been awaiting a new adaptation that stays true to the magic and wonder of the original books. And with the recent announcement of Greta Gerwig taking on the task of directing a new Narnia film, there is much excitement and anticipation building.
One of the biggest challenges in adapting the Narnia series has always been the character of Susan Pevensie, one of the four siblings who first enter the magical world through a wardrobe. In the original books, Susan is portrayed as a sensible and practical young woman, but in later books, she is criticized for not being as devoted to Narnia as her siblings and is ultimately left behind.
Many fans have argued that Susan’s fate is unfair and sexist, as she is punished for growing up and showing an interest in traditionally feminine pursuits. However, with Gerwig at the helm of the new Narnia adaptation, there is hope that this problematic portrayal of Susan can be reimagined and redeemed.
And with the recent rumors swirling that Florence Pugh, known for her strong and complex female characters in films like “Little Women” and “Midsommar,” is being considered for the role of Susan, fans are buzzing with excitement. Pugh’s talent and range as an actress could bring a fresh perspective to Susan’s character, allowing her to be more fully realized and not reduced to outdated gender stereotypes.
With Gerwig’s visionary storytelling and Pugh’s captivating performance, this Narnia remake has the potential to not only capture the hearts of fans old and new but also to address and rectify the problematic treatment of Susan Pevensie in a way that honors her character and empowers her as a strong, capable woman. The possibilities are endless, and the anticipation for this new Narnia film is higher than ever.
Tags:
Greta Gerwig, Narnia Remake, Susan Pevensie, Casting Choice, Greta Gerwig Narnia, Susan Pevensie Problem, Popular Casting, Narnia Casting, Narnia Susan Pevensie
